PRE2024 3 Group7

Group


Name	Student ID	Major
Isaak Christou	1847260	Electrical Engineering
Luca van der Wijngaart	1565923	Computer Science
Daniel Morales Navarrete	1811363	Applied Mathematics
Jeremiah Kamidi	1778013	Psychology and Technology
Joshua Duddles	1719823	Psychology and Technology

Problem Statement

The Netherlands is currently facing a significant issue related to the aging concrete infrastructure, particularly regarding bridges, viaducts, and underpasses managed by Rijkswaterstaat. While concrete structures were initially designed to last for a long time (approximately 100 years), since around 2005, increasing traffic loads and evolving safety standards have exposed potential weaknesses in older infrastructure. The growing traffic volume and vehicle weight now exceed the original design expectations, and stricter regulations such as NEN 8700 and Eurocodes necessitate a thorough reassessment of the country's existing structures. Unlike new constructions, it is difficult to reinforce older structures, making precise recalculations essential to ensuring their safety and continued functionality. Rijkswaterstaat manages approximately 4,800 bridges and viaducts, part of the 90,000 such structures across the Netherlands, with a total replacement value of €65 billion. Most of these structures were built between 1960 and 1980, making them approximately 60 years old. Even structures such as these that have around 40 years of use left are showing concerning amounts of wear and tear. The issue is compounded by the reality that many of these aging bridges are nearing the end of their technical lifespan. This results in a significant challenge for maintenance, reinforcement, and potential replacement over the coming decades. Between 2040 and 2060, the Netherlands will face a critical challenge in replacing and renovating these aging bridges and viaducts and many of them will require major attention in the coming decades to ensure their structural safety and maintain the reliability of the country’s infrastructure. Rijkswaterstaat faces several challenges in addressing these issues:

Technical Challenge – Ensuring the ongoing safety and functionality of aging bridges and viaducts.
Future-Proofing – Adapting existing structures to meet modern usage requirements.
Limited Resources – A shortage of skilled professionals coupled with an increasing project workload.
Human Safety – Traditional inspection methods are hazardous, particularly for inspectors who need to climb or navigate dangerous parts of the bridge, and traffic closures affect public safety.

To help alleviate some of the workload faced by Rijkswaterstaat, our team proposes a semi-automated data collection system aimed at facilitating the general inspection of concrete bridges. These general inspections are conducted every six years and focus primarily on surface-level analysis, rather than in-depth structural assessments. With approximately 4,800 concrete bridges under its management, Rijkswaterstaat must inspect around 800 structures per year, a considerable burden on current human resources. Our proposed solution, initially envisioned as a "Crack Detection Robot", seeks to streamline this process by reducing inspection time, costs, and safety risks. The system would eliminate the need for extensive traffic closures and complex setups, especially for difficult-to-access bridges, such as those over water or at significant height. It would rely on wireless, semi-autonomous robots equipped with high-resolution cameras to capture the tens of thousands of images typically required for inspections.

While the original concept focused on designing such a robotic system, the project has since pivoted to researching the feasibility of using thermal cameras mounted on drones to enhance crack detection capabilities. This new direction explores whether drones offer a more effective and practical alternative for bridge inspections by using thermal cameras to make crack detection easier. Thermal cameras also come with the added benefit of providing more information on the cracks such as depth and width which would normally be inaccessible for general inspections.

USE

Users

Bridge inspectors and maintenance personnel have specific needs when it comes to robotic inspection systems, as highlighted in an interview with Dick Schaafsma, the highest strategic advisor for Bridges and Viaducts at Rijkswaterstaat. He pointed out that one of the biggest challenges in bridge inspections is the necessity to close bridges for safety, which can lead to significant traffic disruptions. For instance, closing a bridge for inspection can reroute large trucks through city centres, causing potential hazards and public backlash if accidents occur. Therefore, Rijkswaterstaat seeks a system that allows for inspections without shutting down bridges. Additionally, inspecting high or waterway bridges presents challenges beyond safety, as they often require specialised equipment and can be hard to access in certain (drones in this case) areas. Drone-based inspection systems—like the ones being explored in this project—can significantly reduce inspection time, improve safety, and minimize traffic impact. However, implementing such systems also requires training current inspectors to use and interpret data from this new technology. In the case of drones, there is already simulation software available to support pilot training. There are also challenges, such as maintenance costs, legal restrictions (e.g., licensing requirements, flight path limitations near sensitive areas like military zones), and safety concerns when operating drones near live traffic. Despite these hurdles, Schaafsma expressed optimism about the potential of robotic and AI-driven systems in improving inspection quality, while emphasizing that human oversight remains essential to ensure the reliability of assessments.

Society

Society and users are largely intertwined regarding this technology. Bridges in the Netherlands are under government supervision; the government is a societal stakeholder and partly a user. Dick Schaafsma emphasized that they might not directly be a user but may instead outsource the actual flying to professional drone pilots which will be accompanied by inspectors. Rijkswaterstaat can then use the data to form reports on each specific bridge as is done now. In addition to the governmental agencies that will obviously benefit from this technology, so will the general public, another significant stakeholder from a societal perspective. When correctly implemented, the general public can enjoy safer bridges and a more reliable traffic network. Road users might have concerns or questions when, for example, they soon see flying drones above the road or around the bridge conducting inspections. For this reason, it is important that the government informs communities about the benefits and implementation of this technology, as well as the associated (low) risks involved for the general public. Lastly, the technology must comply with laws, regulations, and standards that are already in place regarding safety and reliability.

Enterprise

From the enterprise perspective, we have the drone manufacturers and maintenance companies, as well as the companies that will apply the drone technology—either Rijkswaterstaat itself, if this part is internal, or another organization with extensive knowledge of drone use and its own specialized personnel. These organizations are paid by Rijkswaterstaat to monitor and inspect bridges and to provide reliable data that can be used to make informed decisions. This is the approach mentioned by Dick Schaafsma. If this inspection method proves to be cheaper or more beneficial, then the companies providing the inspection technology will become economically viable. This could potentially result in current bridge inspectors at Rijkswaterstaat being less utilized, as the inspection process becomes outsourced, thereby affecting the roles of these employees.

Objectives

Explore the ethical and legal considerations associated with using drone-based bridge inspection systems, including issues of data privacy, regulatory compliance, workforce displacement, and liability in infrastructure monitoring.
Investigate and compare methods for surface defect detection in concrete bridges, with a focus on the potential of thermal imaging.
Assess the viability of thermal cameras for detecting cracks and structural anomalies in the Netherlands.
Evaluate the applicability of AI-based techniques for enhancing defect detection with thermal cameras.
Compare the efficiency, safety, and reliability of drone-based infrared inspection systems with conventional manual inspection methods, including time, cost, and data quality aspects.

Identify the practical limitations and operational challenges of implementing drone-based inspection systems, such as legal flight restrictions, training requirements, and environmental conditions.

Note: Due to the time constraints of the course not all objectives are possible, the focus was on researching if the thermal camera approach is even possible

Approach, milestones and deliverables

To address the problem statement and meet the identified user needs, our approach focuses on researching and evaluating the potential of drone-based thermal imaging systems for bridge inspection. Instead of developing a complete robotic solution, our goal is to investigate the feasibility and added value of this specific technology in enhancing surface crack detection. A lack of research for this specific use of thermal cameras especially for colder climates like the Netherlands along with their added benefits like cost, AI integration etc. inspired the team to do the research.

We aim to design a conceptual framework for a drone-based inspection system that leverages thermal cameras and AI-based image analysis to detect and localize structural cracks in concrete bridges. The system is intended to support infrastructure maintenance by improving data collection efficiency, reducing safety risks, and enabling more predictive maintenance planning.

We will carry out the first cycle of a multi-phase development process, consisting of the following key phases:

Research & Requirements Gathering
- Conduct interviews with relevant stakeholders (e.g., bridge inspection experts at Rijkswaterstaat)
- Review current technologies in drone-based inspection, infrared imaging, and defect detection in concrete structures
Technology Exploration
- Analyse the use of thermal cameras for crack detection
- Investigate AI-based methods for detecting and classifying and analysing surface defects
Conceptual Design & Proof of Concept
- Develop a conceptual system model integrating drone, sensor, and AI components
- Make a test plan and test the ability of thermal cameras in real life environments
Conclusions
- Results and conclusions of the tests
- Limitations of the design, technology and the approach
- Future research to complete

Planning


Week	General plan	Reached?
1	Problem statement Users Approach/deliverables State of the art	reached
2	Contact users interview users Adjust week 1 content accordingly specify project	reached
3	Interview users Adjust week 1 content accordingly and specify project Begin technical design	reached
carnival week	Finish all parts of technical design discussion and the design as a whole
4	System overview and design Technical Design	reached
5	Finish Technical design Start actual experiments	reached
6	Actual experiments and results	reached
7	Critical evaluation of the design performance and utility Make presentation Finish the wiki Conclusions Future research	reached

Interview 1

Questions:

General introduction of people and project.
Getting informal consent or formal if needed GDPR compliance (can we use the interview and the answers we get, anonymity, can we record, etc.).
Can you walk us through the entire inspection process from start to finish? What technologies, tools, and expertise are involved?
How often are different types of structures inspected?
For a typical inspection, how many people are involved, and what are their roles?
What factors influence how long an inspection takes?
Do you foresee a need for more personnel in the future, or is automation a priority?
What is the total number of bridges and viaducts under Rijkswaterstaat’s responsibility, and how many are inspected annually?
What types of defects are most common, and which are most critical to detect early, and which need to just be documented or monitored (introduce the structure of assessment)?
Do inspectors currently use any AI or image processing for crack detection, or is it all visual/manual?
How are defect reports documented? What data is collected?
Are there known cases of critical failures or near-misses due to undetected defects?
What are the most common failure points in aging infrastructure?
What would an ideal crack detection tool look like in terms of usability, accuracy, and integration with current workflows?
Would a semi-autonomous system (human-in-the-loop) or a fully autonomous one be preferable?"
What environmental challenges should a robotic system be designed for (e.g., rain, dirt, lighting conditions)?
What would be the biggest barriers to adopting an automated crack detection system? (is something like certification needed for drones for example?)
What would be an acceptable price range for such a system?
Set up date for second interview to review our design (finish design before this date and send specifications to interviewee) Date: discuss with team.

Interview Summary:

Bridges last in theory 100 years, but right now they’re already experiencing problems with bridges that are no where close to 100 years (40-60 years old). But let’s say in the most favorable situation, that a bridge lasts 100 years, then they should be replacing one bridges per week (5000 bridges, 100 years), but they’re not reaching this number by far of course. Well to form prioritization within these bridges inspections are held. Also this is a massive task for which they lack the expertise but also the funds to be upscaling greatly, which means that they’re going to have to utilize automation and maybe robotization.

Each bridge has a general inspection every 6 years, but when they see during these global inspections that a bridge is deteriorating fast, they perform specific inspections to see how urgent it really is. In these inspections they might do certain tests like a ‘hammer’ test and they try to clean something here and there.

What they’re not looking for really, is drones for usage in these 6 years-inspections, but rather for inspections in which an inspection would otherwise lead to having to obstruct bridges, redirect traffic, but also in cases where a human inspection could be dangerous. Things like a high inspection for corrosion at the top of a spanning bridge (not sure if this is the right term, a bridge that has these cables that keep it up), this would need the bridge to be obstructed or for catching nets to be installed. This all costs a lot of time and money but maybe the biggest problem is the closing and redirecting of traffic. They’re looking for inspection and detection methods that don’t lead to traffic unsafety.

There also might be use cases for very small robots for places where humans can’t reach.

Drones on the other hand would be of great use for bridges over water: They would otherwise need a ship and the river would have to be blocked off. On the flip side, drones are difficult to use because of restrictions: For bridges near the airport, army bases, royal house, etc it is very unlikely that drones are viable since these facilities don’t allow them near them. Also flying a drone in the dark might be hard, and they do a lot of inspections at night because there is less traffic to reroute.

Scholvorming (=’Schol’forming): Something like loose pieces of concrete kind of breaking off and possibly falling. Normally in a specific/specialized inspection, they could test this by hitting a hammer on a piece of bridge and if these come loose, then there is a problem. A drone is not able to do these more physical tests.

An inspection tipically takes half a day, so they need to make sure to look at the right and important things: These areas of importance have to be defined before hand. But besides this they also look globally at it to inspect new cracks that they did not know of before. A drone would also have to do this: making a lot of pictures, but mainly of the important parts of the bridge where points of concerns lie.

An example of a more specific test is for example how the bridge reacts to vibrations of traffic. For this a drone might need certain sensors, highspeed camera’s. But on the other hand: drones are of limited utility with these specific inspections because they often need some sort of physical tests or actions are required (like the hammer test, ‘plakstrook ophangen’, or cleaning).

Internal cracks: These are not checked during global inspections but they might be during the more specific inspections when these internal cracks are suspected.

Tand-nok: Some sort of design where there are very tight nooks and crannies. These things are not included in modern designs anymore, because they’re very hard to inspect.

Every type of bridge has their own problems:

· Moving bridges have problems with operatingsystems, malfunctions. These can also lead to bridges physically breaking down, if a break system fails it can over extend.

· Steel bridges deal with steel fatigue

· Concrete bridges and viaducts (they have by far the biggest number of these), can deal with problems being non-reinforced concrete, or badly reinforced concrete, which can lead to forces being to big and causing cracks and corrosion of the reinforcements (steel within concrete).

Margins and cracks are very small: cracks of 0.2mm can be fine for now, but 0.3mm can be too big and need action. Spider cobwebs can be seen as a crack by AI. How to solve this?

Research

To solve the problem stated in our Problem Statement we researched and defined a system and its needs. This includes components and parts outside of our specific research direction, but that would be needed for real-life implementations, and serve as context for our further research. Most of the results here are based on the state of the art literature studies. This section will include the different topics: Movement, Detection Method, Autonomous Flight System and Final Considerations.

Movement

As part of our solution to the problem statement above, we initially defined two options for types of robots, namely Drones and Grounded Robots. In this section we will discuss some advantages and disadvantages of each solution.

Drone robot

A drone has great application in bridge inspection and mapping, as the problem statement mentions that in a normal inspection, the bridge would have to be (partly) closed off for the duration of the inspection, and difficult and sometimes unsafe methods have to be used like aereal work platforms or climbing up the bridge columns to carry out the bridge inspections. A drone would eliminate the dangerous situations humans would otherwise have to be in and it would limit the closing of bridges for traffic, which from our interview with Rijkswaterstaat turned out to be a major hurdle in conducting these bridge inspections. Also bridges over water can be easily inspected using drones. A drone has some limitations though, as it can only carry a limited amount of weight and the combination of flying with this weight and many sensors requiring a power source can mean that the operating time is limited. This is one main problem that should be looked into when choosing for a drone as the method of bridge inspection.

Grounded robot

Grounded robots offer a reliable alternative for bridge inspections, especially when it comes to stability, endurance, and power availability. Unlike drones, they are not restricted by weight constraints in the same way, allowing them to carry heavier and more powerful batteries, additional sensors, and onboard computing units. Grounded robots can also operate for longer periods since they are not subjected to the high energy demands of sustained flight. Grounded robots however have more difficulty in reaching tight nooks and crannies and, more importantly, the undercarriage of the bridge. Vertical surfaces like the sides of the bridge are possible, however, through various recent technologies like leveraging vacuum to stick to the wall, but these technologies are still risky to use when hanging upside down. This technology can be found in the State of the Art section under [15] Automated wall-climbing robot for concrete construction inspection and [16] Novel adhesion mechanism and design parameters for concrete wall-climbing robot.

Detection methods

Based on an interview with Dick Schaafsma from Rijkswaterstaat, general-purpose bridge inspections primarily involve surface-level visual assessments conducted by inspectors without the aid of advanced tools. The inspection process requires a thorough examination of the entire structure, during which inspectors capture thousands of photographs, focusing on areas prone to cracks and other signs of wear and tear. Additionally, the team was advised that the use of AI in government-related agencies presents challenges and may not be ideal. Due to these constraints, the inspection methodology is inherently limited in scope and must, at a minimum, incorporate a high-quality camera capable of capturing high-definition close-up images of cracks and defects as small as 0.2mm in width. It was indicated that cracks of this size begin to pose structural concerns. Furthermore, inspectors are responsible for identifying aesthetic issues, reinforcing the necessity of a high-resolution camera. The camera must also be lightweight and compact to meet the technical requirements of the inspection process.

Another interesting choice for detection methods would be the use of thermal and laser depth cameras. The temperature contrast between the interior and exterior of a crack can facilitate crack detection while providing additional insights into its shape, size, depth, and severity. Moreover, the high colour contrast generated by thermal imaging—such as infrared cameras—can simplify image processing and may prove beneficial when integrated with AI-powered image analysis models. A depth camera can further enhance assessment accuracy by estimating the approximate depth of cracks, allowing inspectors to better evaluate structural risks and distinguish genuine cracks from superficial or aesthetic surface imperfections.

In order for the detection system to work effectively using remote control some steps must be taken. The drone/grounded robot must have a low-latency, first-person view (FPV) flying camera in order for the controller to be able to manually navigate if needed. The thermal and depth cameras complement this FPV camera in detecting cracks since it is usually of limited resolution. Once a crack has been detected the high quality camera the thermal camera and the laser depth camera can be used to take pictures which are stored locally and can be downloaded once the inspection is over for further analysis and discussion.

User Interaction

One other important consideration for an application like ours, is the system controls. State of the Art drones are capable of autonomous flying and wind-and-weather correcting behaviour. It would be able to create its own flight path, making sure to capture the bridge and keeping a set amount of distance from the bridge. It would ease the workload of the engineers and no trained drone flight specialists would have to be utilized. The drone could be deployed while the engineers do some manual inspections or focus their attention to other aspects of such an inspection. So the question is, does our application benefit from such an Autonomous Flight System. Here, a few things have to be taken into consideration. First of all, the inspections are carried out by engineers and specialists who have prepared for such an inspection by analyzing the weakpoints of a bridge, and they possess critical knowledge of the internal forces working on bridges. An autonomous drone would miss these bridges, and will divide its attention and its camera work differently than one of these engineers. Second of all, with the thermal camera collecting valuable information about the location and size of cracks, a human controller would be able to efficiently spot cracks and for example fly closer, to make sure it is captured in enough detail and given the appropriate amount of attention. After these considerations, we believe these two benefits of manually flying the drone outweigh the ease-of-use when choosing for an Autonomous Flight System.

Final Considerations

Battery and battery life

Choosing the ideal battery for your robot is crucial when it comes to optimizing its performance and longevity. Battery life depends on a few factors, and there are a few options to choose from. The main 3 types suitable for batteries in robots are: (1) Li-Ion, a lithium-ion battery (2) Li-Poly, a lithium polymer battery and (3) NiMH, a nickel-metal hybride battery. In this section we will discuss what batteries are best for drone applications, under which we will consider a heavy drone due to an integrated water reservoir.

In an article [x] , Radek Jarema analyzed different the different types of batteries for both grounded and drone robots, based on their unique properties. He writes that since weight is a big constraint, NiMH is not suitable because of its inferior energy-to-weight ratio compared to lithium batteries. Jarema mentions that Li-poly batteries are often chosen over Li-ion batteries in drone applications for its durable design and high discharge current.

The last considerations regarding battery and battery life is the flight time. For our application, the bridge inspections can last a few hours, so the batteries should last a substantial enough time that the inspection can be carried out with one or two battery swaps. For this, the inspectors would have to have pre-charged batteries prepared.

Communication methods

From research, two communication methods stood out for our application. These are ZigBee and Wi-Fi, and are explained and

ZigBee, particularly XBee modules, operate on low power and is ideal for sending small amounts of telemetry data (such as GPS coordinates, battery status, or sensor readings). It typically works in the 2.4 GHz or sub-GHz frequency bands, with a range of up to 1-2 km (for high-power versions like XBee Pro). Due to its low data rate (up to 250 kbps), it is not suitable for transmitting high-bandwidth data like video but is great for command and control signals.

Wi-Fi offers a higher data rate (up to several Mbps) and is commonly used in drones for real-time video streaming, telemetry, and even remote control via apps or computers. However, standard Wi-Fi modules (ESP8266, ESP32, or Raspberry Pi’s built-in Wi-Fi) usually have a shorter range (typically 100-300m) unless paired with high-gain antennas or long-range Wi-Fi modules. 5 GHz Wi-Fi provides faster speeds but reduces range compared to 2.4 GHz Wi-Fi. This sending of data is optional however, as nowadays local storage is highly space-efficient and the drone could be designed to include a local storage that can hold the thousands of images the drone would make during an inspection.

Using a combination of the two technologies will allow for optimal communication between user and drone where XBee handles control signals and Wi-Fi can be used to transmit video and additional data.

Weight

Weight is a crucial factor for drones. It significantly impacts flight duration and stability. Each added gram requires additional thrust, leading to faster battery depletion.

For drones, the main components that contribute to weight include:

Battery pack
High-speed cameras and sensors
Protective casing and structural frame
Communication (and possibly GPS) modules

A balance must be struck between weight and functionality to ensure the drone can carry out its inspection tasks without compromising flight time.

System Architecture Overview

Figure 1: System level overview

In order to investigate the potential of thermal imaging in drone-based bridge inspections, our team went beyond theoretical research and developed a working prototype. The prototype allowed us to explore the full functionality of a crack detection system, test core ideas in real environments, and evaluate whether thermal imaging could be a viable alternative or enhancement to existing bridge inspection techniques.

At the core of our concept is a modular system designed to capture thermal data, enhance visual contrast for crack detection, and enable both onboard and off-site image processing. The architecture of this system is illustrated in the figure and is composed of the following components:

Thermal Imaging Module

Thermal imaging offers specific advantages over traditional RGB visual inspection. Standard drone-mounted cameras have problems distinguishing fine surface-level cracks particularly in changing lighting conditions where thermal cameras have an advantage in detecting temperature differences in distinction between the inner and outer structure of the concrete. Such temperature differences highlight fine surface irregularities such as cracks more prominently, enhancing the detectability of defects which would otherwise be missed. Here are the technical requirements the team considered when choosing thermal cameras as possible enchantments to crack detection. These were derived from the state of the art research and the interview.

Detect cracks as small as 0.2 mm
Compact and lightweight for drone mounting
Low power consumption
Minimal onboard processing to match drone limitations
Compatibility with AI-based post-processing
Non-destructive and passive operation
Effective crack visibility when mounted on drones

Low latency and high frame rate for smooth data capture

A thermal camera aligns well with the system requirements due to its compact size, lightweight design, and low power consumption compared to alternative technologies such as LiDAR or X-ray imaging. Thermal cameras can be either passive or active, with the latter emitting infrared light that does not interfere with the environment or pose risks to humans. Additionally, onboard processing demands are minimal, and post-processing can be performed quickly, enabling low-latency analysis. Many available thermal imaging options offer high frame rates, low latency, and sufficient resolution to detect fine surface cracks as small as 0.2 mm.

One of the key advantages of thermal imaging is its ability to produce strong color contrast in heat maps, which aids visual crack detection when used on drones. While using standard cameras on drones can sometimes make defect detection more challenging than traditional visual inspections, the enhanced contrast provided by thermal imaging simplifies visual interpretation for inspectors. Furthermore, these thermal maps offer a more consistent input for AI-based image analysis, improving the accuracy and efficiency of automated crack detection compared to regular RGB images. Finally thermal images can provide extra information on the width and depth of the crack.

In order to detect surface-level imperfections in concrete structures, we utilized the GY-AMG8833 thermal-imaging camera, a low-power and compact infrared sensor. Although its resolution is quite low (8x8), this was the most viable option to use in proof-of-concept development. Due to its sensitivity in detecting minute temperature fluctuations conclusions form these test must been carefully drawn. Thermal imaging is an integral part of this setup since thermal imaging allows temperatures to be highlighted at points of temperature variance across the surface of the concrete. For the final design, it is important to go for a good quality thermal camera, since it allows for better and more reliable crack detection.

Water Spray Module

In addition to enhancing the overall thermal imaging system's performance, we developed and tested a water spray module designed to increase thermal contrast on the concrete surface. Our approach is borne out of simplicity and works as follows: by applying a known quantity of water onto the concrete, we take advantage of the cooling effect created as evaporation and absorption happen, which causes cracks particularly deeper cracks to have different temperatures than the rest of the surrounding material. This establishes an enhanced thermal gradient, making cracks easier to see in thermal images. The water spray system was developed in consideration of the following technical requirements:

Figure 2: Diagram of our water spray module

Light weight and aerodynamic design to ensure not to compromise drone stability.
Symmetry and stiffness to keep fluid movement from destabilising flight.
A minimum capacity of 100ml, adjustable according to the planned number of sprays in one flight mission.
Adjustable spray settings because larger areas of cracks can use greater pressure or extended duration sprays.
Figure 3: Thermal camera used on a path with cracks
Basic aiming system for accurate targeting of the suspected defe ct location.
A monitoring system for a tank to alert users to available capacity and utilization.
Compatibility across different quadcopter frames, allowing for modular integration into different platforms of drones.

This module was driven by the microcontroller and was user input-responsive through ground control. Operators were free to decide whether and when to spray water from any location depending on live visual and thermal feeds. This flexibility allowed for selective increase in crack detectability in real time within the course of the flight, enhancing real-time inspection.

Microcontroller with Onboard Processing

The Arduino Uno serves as the central hub of the system, responsible for coordinating data from the thermal module and controlling the water spraying module. It also manages onboard image processing, which in a more advanced version could include basic edge detection or temperature differential mapping to identify potential cracks in real-time. The microcontroller sends processed data to a storage unit and streams image feeds to the user’s controller.

Storage

Captured image data is stored locally during flight. This allows the system to maintain a reliable record of thermal images without requiring constant wireless transmission. The stored data can then be retrieved and further analysed either on-site or off-site.

External Analysis

Post-flight, the stored image data is transferred to an external processing device. Here, AI-based image analysis techniques can be applied to identify, classify, and quantify cracks more accurately. This separation of tasks lightweight onboard processing during flight and heavy-duty AI processing off-site allows the system to remain lightweight and drone-compatible, while still achieving high analytical accuracy. Furthermore if the real time data is recorded on a separate mobile device (can only record temperature matrices without colour mapping for example) more detailed analysis could be done on site using complex image processing techniques and AI. This will give the user a second perspective of the bridge in case they miss anything. Whether this external analysis is done on site or off site the fundamental idea remains the same with the only difference being if the data is stored on the drone for processing after the inspection or the data is wirelessly transferred to a nearby device for real time processing.

Controller and User Interaction

The user interface is equipped with three streams from cameras: an FPV (First-Person View) for pilots, a high-definition visual camera, and thermal camera stream. With this multi-camera configuration, inspectors can see in real time and remotely manage both the inspection process and the drone. Users can activate manually the water spray function upon detecting a possible defect. Such interactivity combines human monitoring in the decision process, enhancing reliability and security.

Prototype

Figure 4: Water spray module prototype

Waterspray module

The image depicts the prototype water spray module designed to enhance thermal contrast crack detection in concrete surfaces. Key components include a solenoid valve (to control water release), batteries (providing power), a switch (for manual activation), a water pump (to pressurize and dispense water), and an Arduino Uno (serving as the microcontroller for automated control). The water pump has a pump speed of approx 80 liters per hour, which is believed to provide sufficient pressure for our purposes.Together, these components form a lightweight system that can integrate with any drone. The Arduino coordinates the pump and solenoid valve to spray precise quantities of water based on user inputs from ground control, which are triggered by live thermal/visual feeds during flight. The pump draws water from the reservoir (adjustable for mission needs), and pushes it through a 6mm waterhose to the solenoid valve that controls the water output. This valve is connected via another 6mm tube to the nozzle, ensuring an even spray of water over the crack and surrounding area. In Figure 4, the solenoid valve is not actually positioned between the water pump and the nozzle; this arrangement was used for testing purposes only. The switch provides a manual override and facilitates testing. Figure 6 presents the complete schematic of the circuit.

Thermal camera

Figure 5: Thermal camera

As mentioned earlier, our prototype uses a GY-AMG8833 thermal imaging camera (shown in Figure 5), which is roughly the size of a fingertip and connected to an Arduino via a serial port. To process and visualize the sensor data, we developed a Python script. This script reads real-time temperature measurements from the 8x8 thermal sensor through the Arduino’s serial connection and converts them into a smooth, color-coded heatmap displayed on a screen. It enhances the raw 8x8 grid by interpolating it into a higher-resolution 32x32 grid for smoother gradients, assigns colors from indigo (cool) to red (hot) based on temperature values, and refreshes the visualization in real-time using Pygame. The raw temperature data is simultaneously logged to the console for monitoring. This setup formed the foundation for all testing and experiments.

We envision the water spray circuit and thermal camera as components of a compact module that can be attached to almost any drone, enabling thermal crack detection features. We assume that a mounting system already exists and will focus here on the actual detection technique.

Experiments/Testing plan

To test our theories and to test the viability of a system as described above, we intend to conduct some experiments, or a testing plan. These experiments will be done using a prototype we made using "cheaper" components. This means that these tests might not show the whole truth, and should be taken with a grain of salt, but that the results can be taken as an indication on whether it might be valuable to keep exploring this detection method, especially when using higher quality components.

Timing of testing

Testing at different times

The module should be able to operate despite the outside factors and still give reliable information about the cracks in the concrete. To properly test this an experiment will be conducted on real bridges outside to asses the system. A few parameters were chosen to test the module. The first parameter is the temperature. The temperature has a huge impact on the module as it uses a thermal camera to distinguish the cracks from concrete after spraying the concrete with water. Looking at the climate of the Netherlands, the average highest temperature is 14,5 degrees Celsius throughout the year and the lowest average temperature is 6,3 degrees Celsius. This differs per month as February has the lowest average temperature of just 0,7 degrees Celsius and July has the highest average temperature of 23,1 degrees Celsius. The average temperature in the Netherlands is 10,5 degrees Celsius. The operating temperature of the thermal camera is between 0 and 80 degrees Celsius and the smallest resolution is 0.25 degrees Celsius. This falls in the specified range of 0,7 - 23,1 degrees Celsius. To truly test this out in a real situation the time as a second parameter is also an important factor. How late it is in the day has an impact on the temperature. Due to the strict timetable of the drones, time is of the essence and the drone should work throughout the day. Currently the temperatures fluctuate between 5 degrees to 16 degrees. This is ideal to test out the module as we can test the average lowest temperature and the average highest temperature. There will be three measuring moments. One in the morning when the temperature reaches the average lowest temperature in the Netherlands, so about 6 degrees and the surface of the concrete is still cool. The other measuring moment will happen in the late afternoon when the temperature reaches the average highest temperature in the Netherlands, so about 14,5 degrees. The concrete heats or cools down throughout the day so these timepoints are ideal to see the effectiveness of the module. A third measuring moment will be done either in the early afternoon or the evening as temperatures reach the average temperature in the Netherlands, around 10,5 degrees Celsius. These three measuring moments can provide valuable information about how the module would operate during average circumstances. There will also be a measurement when it's raining or a recreation of the rain to see if external water has an impact on the module. The experiment will take approximately 10 minutes to conduct around a visually distinct crack in the concrete and the experimenter will also record how long it takes for a crack to be noticed by the module. The experimenter will then also try to measure the crack. Taking all of this together will give us an accurate representation of what the module can do.

Figure 6: Circuit schematic of prototype

Testing during different weathers

In addition to variations in time of day, weather conditions play a crucial role in our research on crack detection using thermal imaging. The weather is classified into four primary categories: sunny, cloudy, rainy, and windy. Given the climatic conditions in the Netherlands, where overcast and rainy weather is frequent, it is essential to assess the performance of the infrared camera under these conditions.

Furthermore, weather conditions often occur in combination, leading to additional categories: sunny & windy, cloudy & rainy, and cloudy & rainy & windy. Notably, rainfall always coincides with cloudy conditions. These weather variations significantly influence the thermal response of concrete surfaces, as they can be either dry or wet, depending on precipitation, humidity, and wind-driven evaporation. Assessing the infrared camera's effectiveness across these environmental conditions is critical to ensuring reliable crack detection under real-world scenarios.

Method of testing

The experiments and data collection was done using an Arduino Uno, GY-AMG8833 Thermal Camera Module and the circuit show on the figure to the right. The circuit includes a 12 volt power supply to power the motor and valve, a voltage divider to get the appropriate 5 volts out for the pump and 2 N channel MOSFETs to act as switches to turn on and off the pump and valve. The series resistors on the gate of the transistors are there to limit the current drawn from the microcontroller while the parallel resistance is there to short any stray currents from any intrinsic capacitance the transistor has that may cause it to oscillate between on and off even with no signal from the microcontroller. The camera which is not shown in this circuit is simply connected to the 3.3V, ground and an analogue pin of the microcontroller which can supply more than enough power to the camera as opposed to the pump and valve.

What needs testing for the water spray:

Duration of spray vs water consumed (0.5s, 1s, 2s, 3s, 4s, 5s)
For each duration how any sprays are needed per unit length of crack (defined as 10cm of length)
The 2 above will give total water consumption per unit length
The effect water has on the temperature contrast on cracks
How long this temperature change needs to take effect
How long this temperature change needs to ware off
How long this temperature change needs to reach maximum contrast

What needs testing for the infrared:

As stated before the infrared should be tested for its effectiveness during both different times of the day and during different weathers
The method of testing entails choosing 2 different crack types and taking infrared pictures at different times of the day and during different weather conditions (and concrete conditions) before and after spraying water
In addition an optimal distance between camera and crack should be found (criteria are resolution, crack coverage, drone safety)

Data collection

Experiment setup

What immediately became clear after some testing is that the thermal camera would not have enough resolution or sensitivity to truly capture the temperature differences between small objects and its environment. Therefor the experiment was tweaked in a way to accommodate this. To truly test the effect of the camera would view the bridge on a random point with no crack, the crack with no water, the crack with normal water and the crack sprayed with hot water. The last test was done as the thermal camera relies on the contrast of the temperatures for it to be shown on the screen. Several bridges were picked out, but a few fell through as there weren't any visible cracks. As this was a prototype and the test was meant to prove/disprove if this method of detection would be viable, big cracks were picked so any temperature contrast could be recorded properly. The camera would be pointed at the sprayed spot immediately for at least 10 seconds so any temperature drops could also be recorded. The spot would also be sprayed as generously as possible until the crack was visibly wet. In addition, information about the location would also be recorded which includes the current temperature under the bridge, the temperature as recorded by a weather app, the amount of used hot water, the amount of used normal water, the temperature of the hot water and the temperature of the normal water.

Results

Results of the experiment
Hot water on crack
Cold water on crack
Crack in a bridge
Thermal view of bridge with no crack
Crack in the bridge

Testing material

As seen above the crack as displayed in Crack in Bridge can not be visibly seen on the thermal camera which was also the case of the other thermal cameras. In total for the first round of experiments 4 concrete bridges with visible cracks visited in the region of Eindhoven. Unfortunately due to the limitations of the Dutch weather different weather conditions could also not be checked. The average temperature outside was 13.03 degrees, the temperature of the hot water never went below 50 degrees. Two bridges had paint sprayed on them and two bridges were plain concrete bridges. The cracks varied in size from 0.25 cm to 2.2 cm.

All the initial results proved to be negative as visual inspection confirmed that there was not enough resolution in the camera to capture the crack. Several hypotheses were proposed following these tests results as the water could be cooling down instantly, the concrete didn't heat up quick enough, the concrete cooled down immediately or due to our technical limitations it simple was not possible. The experiment was modified. Initial testing and further testing showed that even with boiling water (90 degrees) the temperature of the sprayed water, and therefor the temperature of the concrete, would drop immediately when exiting the container. Therefor a new more rudimentary experiment was setup which would be able to accommodate all the factors mentioned in the hypotheses. A large gap between two concrete structures would be selected which would take into account the limitations of the camera and would be open for 10x - 100x upscaling. This range was used as the current camera had a resolution of 8x8 whereas in practical settings a thermal camera would have a resolution of 10x - 100x the 8x8 resolution which was used. Hot water would not be sprayed, but directly poured onto the concrete. The hypothetical result would a thermal image of two peaks and one valley, where the valley would be the crack. This accommodated for every variable previously identified as a source of experimental failure while also retaining high ecological validity.

Revised Experiment results

Revised expirement
Concrete structure with gap
Thermal camera results
Thermal camera results with guideline

In the revised experiment the results were positive. A concrete gap with a width of 1.7 cm was found and used as the thermal camera would instantly begin filming. The outside temperature was 13.1 degrees and the temperature of the hot water was 67.5 degrees. In total half a liter of hot water was poured on the concrete. The crack had a width of 1.7 cm and in the upscaled image the width of the gap was 5 pixels. This meant that accounting for a thermal camera with a 800x800 resolution the crack could have a width of 0.17 mm and still be detected. The testing was furthermore tested with other parameters by for example changing the sensitivity of the thermal camera but that resulted in negative results. A median range of 3 degrees was eventually chosen for the best fit. After 11.34 seconds the hot water dissipated and the crack was no longer visible.

Final Conclusions

Conclusion from testing plan

The initial testing plan failed to fully support our theories laid out above, however we found that this was mainly because of hardware limitations. Once we saw that the results were not optimal, and we realized that this may be because of the resolution of the thermal quality, we tried upscaling the surfaces and more importantly the cracks, so it would match the scale of the thermal camera. After doing this, we did some more tests and we found that combining this with hot water being sprayed on the crack, resulted in the camera being able to distinguish the cold insides of the crack from the outside surface, which was heated up more by the hot water, than the inside of the crack. We believe that this shows promising signs of our system working in this way, but as mentioned, further research would be required to fully support our theories and prove that our our system solves the problem statement.

Limitations & Future research

While our prototype successfully demonstrated the feasibility of using thermal imaging for surface crack detection on concrete structures, the limitations of the GY-AMG8833’s low resolution restricted our ability to accurately detect or measure fine cracks, particularly those below a few millimeters in width. To partially address this, we upscaled the test environment, using a 1.7 cm wide crack and applying hot water to enhance the thermal contrast. Combined with pixel interpolation techniques, this allowed us to confirm the system’s ability to identify large cracks under controlled conditions. However, these results do not yet validate performance under realistic, small-scale crack conditions typical of actual bridge structures.

For this reason, future research should focus on conducting properly scaled experiments using higher-resolution thermal cameras capable of detecting cracks in the sub-millimeter range (e.g., 0.2 mm). Additionally, we propose longitudinal testing under varying environmental and lighting conditions, including different seasons, temperatures, and times of day, to understand how external factors influence thermal imaging effectiveness. Such year-round testing would offer valuable insights into the robustness and reliability of drone-mounted thermal inspection systems in real-world applications across the Dutch climate.

Because of the time constraints and limitations regarding this project, we have only conducted tests over the space of 2 weeks, which means we were not able to test during a variety of seasons, temperatures and other weather conditions. Year round testing would be necessary to fully establish the reliability and the accuracy of our system.

Lastly, also because of time constraints, tests were not conducted on the optimal amount of water used, and the optimal distance between the nuzzle and the concrete surface. This is another important aspect in this system design since it would be used on a drone, and the drone cannot spray the wall from too close. If it were to go close to the wall, its cameras would then also not be seeing a lot of surface area at a time and it would make the process quite inefficient. If the system turns out to need quite a lot of water per surface area, another interesting point of further research is tethering the drone to a base point which can supply the drone with water and which would eliminate some of the weight limitations, eliminating the need for a heavy water tank on the drone.

Work Records

Week 1


Name	Hours	Work
Isaak Christou	8	Group meeting, made the wiki page, problem statement, 5 relevant papers summaries in state of the art
Luca van der Wijngaart	6	Group meeting, first start to the Approach section. first 2 out of 5 papers for state of the art section.
Daniel Morales	6	Group meeting, wrote objectives, found 5 relevant papers for state of the art and wrote a summary
Joshua Duddles	8	Group meeting, research problem statement, find and contact users
Jeremiah	8	Group meeting, research and writing summaries

Week 2


Name	Hours	Work
Isaak Christou	1.5	Group meeting, remade problem statement
Daniel Morales	2	Group meeting, remade Objectives and meeting
Joshua Duddles	6	Group meeting, read 3 research paper and wrote wiki state of the art
Luca van der Wijngaart	4	Group meeting, finished the approach section, found 1 more State of the Art paper
Jeremiah	3	Group meeting, wrote down the state of the art

Week 3


Name	hours	Work
Isaak Christou	20	Group meeting, made interview questions + edited the wiki for better structure Remade problem statement Made preliminary technical requirements Research on detection methods and sensors
Luca van der Wijngaart	20	Group meeting, conducted interview, researched and added movement methods, battery and weight constraints to Research section, Summarized 2 more State of the Art papers
Daniel Morales	20	Group meeting, review interview information, adjust objectives based, investigate and write autonomous flight system, investigate understand and write CNN for detecting road cracks on images
Jeremiah Kamidi	20	Group meeting, conducted interview, interview translation + transcribing, started CAD model, added more research and wrote Body part of design
Joshua Duddles	15	Group meeting, conducted interview, added another 2 relevant papers in state of the art, wrote USE part

Week 4


Name	hours	Work
Isaak Christou	10	Group meeting, edited wiki page to fix some structure Communication method Water spray design Water spray requirement
Luca van der Wijngaart	8	Group meeting, rewrote the approach section after change in research scope.
Joshua Duddles	8	Group meeting, research and writing on obstacle removal by drones
Jeremiah	10	Group meeting, researching drone bodies

Week 5


Namr	hours	work
Isaak Christou	25	Group meeting, work on protype design and implementation Wiki edits System architecture Testing methods
Luca van der Wijngaart	15	Group meeting, updated wiki with newly defined project scope. Also helped with the prototype design
Joshua Duddles	18	Group meeting, prototype part of the wiki, research
Daniel Morales	25	Group meeting, prototype design, component selection, wiki
Jeremiah	15	Updated wiki, group meeting

Week 6

Namr	hours	work
Isaak Christou	25	Prototype water spray work
Luca van der Wijngaart	25	Prototype assembly and testing. Writing Arduino and Python scripts for experiments.
Daniel Morales	25	Thermal camera and electrical system assembly and testing
Jeremiah Kamidi	23	Experiments, wiki updating and more experimenting
Joshua Duddles	24	Experimenting, wiki work

Week 7

Namr	hours	work
Isaak Christou	8	Presentation Wiki
Luca van der Wijngaart	15	More prototype testing and writing part for presentation.
Daniel Morales	10	Presentation preparation and wiki editing
Jeremiah Kamidi	10	Worked on the presentation and wiki
Joshua Duddles	9	Worked on the wiki

Week 8

Namr	hours	work
Isaak Christou	8	Wiki
Luca van der Wijngaart	10	Wiki
Daniel Morales	8	Wiki
Jeremiah Kamidi	6	Wiki
Joshua Duddles	10	Wiki

State of the art

Detection

[1] 3D vision technologies for a self-developed structural external crack damage recognition robot

This papers discusses the viability of multiple 3D vision techniques for detecting external cracks in infrastructure. This includes image based methods that only recently gained some adaptability, point cloud based methods that require substantial computational resources and 3D visual sensing and measuring methods such as 3D reconstruction. According to the article all methods presented lack one of three things: weight (the technology is usually to heavy), precision (to 0.1mm accuracy required for diagnosis) and robustness and accuracy. The authors then go to present a new type of automatic structural 3D crack detection system based on the fusion of high-precision LiDAR and camera which is more lightweight combines the depth sensing of LiDAR with the detailed imagery of the camera and has the required real time precision for safety diagnostics.

[2] ROAD: Robotics-Assisted Onsite Data Collection and Deep Learning Enabled Robotic Vision System for Identification of Cracks on Diverse Surfaces

This paper discusses the architecture of ROAD (Robotics-Assisted Onsite Data Collection System) as a means of automatically detecting cracks and defects in road infrastructures. The paper looks into traditional methods of crack detection and their limitations and encourages the use of deep learning in crack detection. The paper also discusses the effectiveness of multiple deep learning algorithms in detecting cracks on roads and concludes that Xception has the best performance with an accuracy over 90% and mean square error of 0.03. More generally the paper claims that deep learning algorithm trained in good datasets outperform the traditional methods. The reason why the authors push for ROAD is due to the lack of automation when it comes to traditionally detecting cracks in roads and therefore introduces ROAD (Robotics-Assisted Onsite Data Collection System), which integrates robotic vision, deep learning, and Building Information Modeling (BIM) for real-time crack detection and structural assessment.

[3] Novel pavement crack detection sensor using coordinated mobile robots

The paper proposes the design of an integrated unmanned ground vehicle (UGV) and drone system for real-time road crack detection and pavement monitoring. A drone conducts an initial survey using image analysis to locate potential cracks, while the UGV follows a computed path for detailed inspection using thermal and depth cameras. The collected data is processed using MATLAB and CrackIT, enhanced by a tailored image processing pipeline for improved accuracy and recall. A crowd-sourced crack database was developed to train and validate the system. Webots software was used for simulation, demonstrating the system’s effectiveness in structural health monitoring. The proposed system offers high mobility, precision, and efficiency, making it suitable for smart city applications.

[4] Pixel-Wise Crack Detection Using Deep Local Pattern Predictor for Robot Application

This study introduces a novel crack detection method using a Convolutional Neural Network (CNN)-based Local Pattern Predictor (LPP). Unlike traditional methods that classify patches, this approach evaluates each pixel’s probability of belonging to a crack based on its local context. The proposed seven-layer CNN extracts spatial patterns, making the method robust to noise, lighting variations, and image degradation. Experiments using real-world bridge crack images demonstrate superior accuracy over existing methods (STRUM and block-wise CNN). The study also explores optimized sampling techniques and Fisher criterion-based training to enhance performance when datasets are limited. The method shows potential for real-time crack detection in robotic vision applications.

[5] Development of AI- and Robotics-Assisted Automated Pavement-Crack-Evaluation System

The paper presents AMSEL, a semi-automated robotic platform designed to inspect pavement cracks in real-time using a deep learning model called RCDNet. The system uses both manual and automated navigation to collect data indoors and outdoors, with RCDNet detecting cracks based on image analysis. Despite some limitations, such as difficulty detecting cracks smaller than 1 mm and issues with lighting and shadow interference, the system provides an efficient alternative to manual inspections. Future improvements include integrating non-destructive testing (NDE) sensors, expanding the use of visual sensors for faster coverage, and developing deep learning models that can fuse data from multiple sources for more comprehensive defect detection.

Robotic surface exploration with vision and tactile sensing for cracks detection and characterization

The paper Robotic Surface Exploration with Vision and Tactile Sensing for Cracks Detection and Characterization suggests a hybrid approach to crack detection by complementing vision-based detection with tactile sensing. The system first employs a camera and object detection algorithm to identify potential cracks and generate a graph model of their structure. A minimum spanning tree algorithm then plans an effective exploration path for a robotic manipulator that reduces redundant movements.

To improve the accuracy of detection, a fiber-optic tactile sensor mounted on the manipulator verifies the presence of cracks, removing false positives from lighting or surface textures. Once verified, the system performs an in-depth characterization of the cracks, pulling out significant attributes such as length, width, orientation, and branching patterns. The two-sensing modality yields more precise measurements than traditional vision-only methods.

Experimental validation demonstrates that this integrated approach significantly enhances detection accuracy while reducing operating costs. By optimizing motion planning and reducing reliance on full-surface scanning, the system offers a more efficient and less expensive method of automated infrastructure inspection and maintenance.

Complete and Near-Optimal Robotic Crack Coverage and Filling in Civil Infrastructure

The paper Complete and Near-Optimal Robotic Crack Coverage and Filling in Civil Infrastructure proposes a new approach for autonomous crack inspection and repair with a simultaneous sensor-based inspection and footprint coverage (SIFC) planning scheme. The method blends real-time crack mapping and robot motion planning for effective and complete inspection. Integration of sensing and actuation through sensing and actuation integration makes the system efficient by avoiding redundant motion and providing optimal crack coverage.

The robot takes a two-step strategy, first, onboard sensors are used to detect and map cracks in real-time and calculate an optimal path of coverage using a greedy exploration algorithm. Second, a robotic manipulator follows the path and dispenses crack-filling substances where needed. The algorithm adjusts its path in real-time based on new cracks, allowing the system to react to irregular and complex surfaces without pre-computed structural maps.

Experimental results reveal that this system significantly improves the detection and effectiveness of crack repairs at a lower cost of operation. Through ensuring total crack coverage with minimal travel distance, the system outshines traditional procedures, making it a promising alternative for extensive rehabilitation of infrastructure.

Crack-pot: Autonomous Road Crack and Pothole Detection

The paper Crack-Pot: Autonomous Road Crack and Pothole Detection proposes an autonomous real-time road crack and pothole detection system using deep learning. This system employs a neural network architecture to handle road surface textures and spatial features, enabling the discrimination between damaged and undamaged areas. The approach improves the accuracy by reducing the misclassification due to environmental factors like lighting variations and surface unevenness.

The detection is carried out by capturing road images through a camera-based system mounted on an automobile or robotic platform. The images are input into a convolutional neural network (CNN) which identifies cracks and potholes based on their unique structural features. Compared with traditional thresholding-based methods, the learning-based approach is made versatile under different conditions with better robustness against occlusions, shadows, and background noise.

Experimental results show that the system achieves high accuracy of detection while operating in real-time, making it feasible for monitoring large-scale infrastructure. By automating road inspection, this method enhances efficiency and reduces the need for manual inspections, resulting in more proactive and cost-effective road maintenance procedures.

Visual Detection of Road Cracks for Autonomous Vehicles Based on DeepLearning

The research article Visual Detection of Road Cracks for Autonomous Vehicles Based on Deep Learning and Random Forest Classifier presents a high-tech image-based approach towards detecting road cracks based on the combination of deep learning and machine learning methods. The study integrates convolutional neural networks (CNNs) with a Random Forest classifier to improve accuracy in identifying faults in road surfaces. The method is intended to assist autonomous cars in driving over faulty roads while contributing to the maintenance of the infrastructure as well.

The system utilizes three state-of-the-art CNN models: MobileNet, InceptionV3, and Xception, trained on a 30,000 road image dataset. The learning rate of the network was tuned in experimentation to 0.001, yielding a maximum validation accuracy of 99.97%. The model was also tested on 6,000 additional images, where it recorded a high detection accuracy of 99.95%, demonstrating robustness under real-world conditions.

The results demonstrate the hybrid deep learning and machine learning technique significantly enhances crack detection accuracy compared to traditional methods. With its integration into autonomous vehicle technology or roadway maintenance initiatives, the technique offers a highly scalable, effective solution for real-time infrastructure monitoring and defect detection.

Article Deep Learning Based Pavement Inspection Using Self-Reconfigurable Robot

The paper Deep Learning-Based Pavement Inspection Using Self-Reconfigurable Robot introduces a robot system utilizing deep learning to conduct real-time pavement inspection and defect detection. The robotic system is centered on Panthera, a self-reconfigurable robot utilizing semantic segmentation and deep convolutional neural networks (DCNNs) for the detection of road defects and environmental obstructions such as litter.

The inspection process has two primary components: SegNet, a deep learning model that delineates pavement areas from other objects, and a DCNN-based defect detection module that detects different types of road defects. To enhance the system's usability in practical applications, it is integrated with a Mobile Mapping System (MMS) that geotags cracks and defects detected, allowing for precise location tracking. The Panthera robot has NVIDIA GPUs, which enable real-time processing and decision-making functions.

Experimental testing confirms that the system is highly accurate in detecting pavement damage and functions well under diverse urban environments. The technique not only optimizes the effectiveness of autonomous road maintenance and cleaning but also provides a scalable means for intelligent infrastructure management, reducing the need for manual inspections.

Vehicle/movement

[11] The Current Opportunities and Challenges in Drone Technology

This recently published paper discusses the the advancements that Drone Sensor Technology and Drone Communcation Systems have made, after which it defines some opportunities and challenges that the field of drone technology faces and it draws some conclusions on where it thinks this technology is headed and the general importance this field will have in certain industries.

It discusses the applications of Drone technology in the sectors Agriculture, Healthcare, and Military & Security. According to the paper, drones have already started being a critical too in the Agriculture sector as they perform crop monitoring and analysis to detect diseases early, leading to improved yields, but also Livestock monitoring by tracking movements and using thermal cameras. Healthcare has started using drones for medical supply deliveries and emergency response: drones can easily get crucial supplies to hard-to-reach areas. Drones are also being used by the military for surveillance and reconnaissance and higher precision of (air-)strikes. This leads to less colateral damage and enhances battlefield efficiency.

The paper states some opportunities pertaining to these previously mentioned sectors, but more interestingly it states some challenges that it believes drone technology faces, that can be important to many sectors besides these 3. It mentions that current regulations and legal frameworks limit the use of drones immensely, and drones are prone to cybersecurity threats, being at the risk of hacking and unauthorized control. It also names some technical limitations such as limited battery life, payload capacity and drone costs being high.

[12] Drone Technology: Types, Payloads, Applications, Frequency Spectrum Issues and Future Developments

This paper discusses various aspects of drone technology, such as types of drones, levels of autonomy, size and weight, payloads, energy sources, and future developments. Although the paper was published in 2016—9 years ago—a lot of the core technology remains the same, albeit more efficient and better built. Here, we'll summarize some parts briefly.

There are three main classes of drones: fixed-wing systems, multirotor systems, and other systems, such as ornithopters or drones using jet engines. Fixed-wing and multirotor systems are the most used and important. The first class is built for fast flight over long distances but requires a landing strip to take off and land. Benefits of the latter include reduced noise and the ability to hover in the air.

The United States Department of Defense distinguishes four levels of autonomy: human-operated systems, human-delegated systems, human-supervised systems, and fully autonomous systems. A distinction is made between autonomous systems and automatic systems: "An automatic system is a fully preprogrammed system that can perform a preprogrammed assignment on its own. Automation also includes aspects like automatic flight stabilization. Autonomous systems, on the other hand, can deal with unexpected situations by using a preprogrammed ruleset to help them make choices."

This requires energy. There are four main energy sources: traditional airplane fuel, battery cells, fuel cells, and solar cells. Airplane fuel is mainly used in large fixed-wing drones, while battery cells are the most common in smaller multirotor drones. Fuel cells are not widely used—one reason being that these types of cells are relatively heavy—so only larger fixed-wing drones can be equipped with them. Solar cells are also not often used in the drone industry. Low efficiency is one of the reasons for their limited application.

Lastly, the paper expects three major developments in the coming years in terms of drone technology, namely miniaturization (i.e., smaller and lighter drones), greater autonomy (i.e., more autonomous drones), and swarms (i.e., more drones that can communicate with each other).

[13] ANAFI Ai Photogrammetry

Parrot is a leading French drone manufacturer that focuses exclusively on professional-grade drones, offering two options: the ANAF Ai and ANAFI USA. As they say, “With our professional drones, we provide best-in-class technology for inspection, first responders, firefighters, search-and-rescue teams, security agencies, and surveying professionals.” Going into more depth, the ANAF Ai is capable of photogrammetry, which is the process of creating visual 3D models from overlapping photographs. Some key features of this drone are its 48 MP camera that can capture stills at 1 fps, compatibility with the PIX4D software suite, in-flight 4G transfer of data to the cloud, and the ability to create a flight plan with just one click. The ANAFI Ai is equipped with a camera that tilts from -90° to +90°, making it ideal to inspect the underside of bridges. Perception systems ensure the safety of the flight plan, so users don't need to worry about obstacles. The ANAFI Ai avoids them autonomously.

[14] DJI Bridge Inspection

Another leading drone manufacturer, and by far the biggest, is a Chinese company called DJI (short for Da-Jiang Innovations). This company offers an immense amount of products to the market—not just drones, but also power supplies, handheld cameras, and drive systems for e-bikes. Their primary specialization, however, is drones. Their range is vast, encompassing consumer camera drones, specialized agriculture drones for crop treatment, delivery drones, and enterprise drones for business use cases. On their website, they describe the different use cases and provide corresponding "solutions." These solutions combine a base drone platform, various payloads, software packages, and recommended workflows. For example, for bridge inspection, they provide three different solutions. One of these, their "Bridge Digital Twin Asset Management" solution, features the Matrice 350 RTK base drone (weighing approximately 6.47 kg) with payloads such as the Zenmuse P1—a 45 MP full-frame camera—and the Zenmuse L2, a LiDAR sensor. In addition, DJI Pilot 2, DJI Terra, and DJI Modify are software packages that integrate seamlessly to create an efficient workflow. Other solutions involve fewer sensors and smaller drones, allowing potential buyers to customize the possibilities.

[15] Drone-enabled bridge inspection methodology and application

This paper explores using drones for inspecting bridges as an efficient, low-cost alternative to traditional methods. With many bridges deteriorating, as noted by the ASCE, the study focuses on a timber bridge near Keystone, South Dakota, using a DJI Phantom 4. Researchers developed a five-stage inspection method based on extensive literature review and current practices. The results showed that the drone produced measurements and images comparable to those of conventional inspections while reducing time and risk to inspectors. The study demonstrates that drone technology can support legally mandated inspections and offers potential benefits in cost savings, safety, and data quality for future infrastructure assessments.

[16] Bridge Inspection with an Off-the-Shelf 360° Camera Drone

This study by Andreas Humpe examines how an off-the-shelf 360° camera drone can be used to inspect bridges. The research shows that using an easily available drone equipped with a 360° camera is a practical and cost-effective alternative to traditional inspection methods. The drone captures comprehensive, high-quality images from all directions, making it easier to spot damages and structural issues. By reducing the time and risk involved in manual inspections, this approach can improve safety and efficiency. The findings suggest that such technology could play a significant role in modernizing bridge inspection practices and supporting reliable maintenance decisions.

Communication

[17] THz band drone communications with practical antennas: Performance under realistic mobility and misalignment scenarios

This recently published paper explores the role of Terahertz (THz) band communications in 6G non-terrestrial networks (NTN), focusing on drone-based connectivity, spectrum allocation, and power optimization. Drones are expected to act as airborne base stations, enabling high-speed, ultra-reliable connectivity for applications like surveillance, sensing, and localization.

The study evaluates the true performance of THz drone links under real mobility conditions and beam misalignment, finding that while data rates of 10s to 100s of Gbps are achievable, severe performance degradation can occur due to misalignment and antenna orientation changes. It analyzes three channel selection schemes (MaxActive, Common Flat Band, and Standard) along with two power allocation strategies (Water-Filling and Equal Power), identifying a commonly available THz band for stable transmission.

The paper highlights major challenges for THz drone communications, including frequency selectivity, beam misalignment, and mobility-induced disruptions. It emphasizes the need for active beam control solutions to maintain reliable performance. While THz technology offers vast bandwidth potential, overcoming alignment and stability issues is critical for practical deployment in 6G drone networks.

[18] Redefining Aerial Innovation: Autonomous Tethered Drones as a Solution to Battery Life and Data Latency Challenges

This article explores the idea of using drones connected to a power supply through tethering it to one. It mentions that flight durations typically range between 20 and 55 minutes, and that this could result in having to frequent recharging or battery replacements which disrupts operations. Additionally it mentions that this way, communication can also be done through this tether which removes the problem of data latency and would allow for more responsive controls and transferring data like images to an external storage, removing the need of a SSD card or other storage module on the vehicle itself.

The study highlights the technological advancements that enable tethered drones to operate efficiently. Modern tether designs incorporate lightweight yet durable materials capable of transmitting power and data at high speeds. Some models use fiber optic cables to achieve data transmission rates of up to 10Gbps, significantly reducing latency. Despite these advantages, tethered drones come with their own set of challenges consisting of mobility restrictions due to the physical tether, but also the vulnerability to environmental conditions such as winds and rain. It proposes future potential developments such as improved tether materials, better autonomous navigation and integrating 5G technology. It concludes stating that it is a innovative solution in the UAV technology for applications requiring long flights in which battery life is an issue.

Main system

[19] Drone-Based Non-Destructive Inspection of Industrial Sites: A Review and Case Studies

This paper explores the increased use of unmanned aerial vehicles (UAV) for inspecting industrial sites through non-destructive inspection methods. It speaks of advantages over manual inspections performed by humans in the forms of enhanced safety, cost reduction and easier access to hard-to-reach areas. It discusses different inspection techniques like thermography, visual inspection and ultrasonic mapping. The paper also identifies challenging areas such as battery limitations, vibration effects on sensors and environmental factors affecting data accuracy.

The paper also presents different applications of these UAV inspections including bridge condition assessments and specifically mentions that drones can assist in detecting cracks, delaminations and corrosino in concrete structures such as bridges and buildings, being of great use in the field of preventive maintenance. It gives a few case studies in this bridge maintenance sector as well as other sectors, and finally emphasizes the need of further research and development in drone stability, sensor accuracy and automated defect detection algorithms.

[20] Automated wall-climbing robot for concrete construction inspection

In this article highlights the development of an automated wall-climbing robot designed for inspection of concrete structures. The way the robot sticks to a surface is with the use of a negative pressure adhesive module. A flexible skirt seal is attached at the bottom of this vacuum to prevent air from escaping and maintaining negative pressure. The robot also has wheels on the bottom. When climbing curved surfaces the negative pressure presses the robot against the surface and allows it to move over shallow grooves. The authors further specify that the robot is equipped with a RGB-D camera and deep learning algorithms to detect flaws. In the robot is a chip which communicated over the WIFI with a server with a dedicated GPU where the deep learning algorithms get applied. The camera and the motion control is connected with an USB to the chip and can be remotely controlled. When detecting the surface the robot creates a 3D surface map.

[21] Novel adhesion mechanism and design parameters for concrete wall-climbing robot

In this paper a prototype robot gets built which can climb on reinforced concrete structures using a non-contact magnetic adhesion mechanism. The robot is primarily built for non-destructive testing of the concrete. The authors argue that using such a wall-climbing robot can make inspections safer, more cost effective and more efficient. The robot has four wheels and the adhesion module fixed underneath. The authors go over the simulations they have done and eventually go for neodymium magnets with grey cast iron. The magnets are orientated in such a way that a magnetic field gets created with one side of the magnets with their north side pointed to the rebar in the concrete and another magnet with their south side pointed at the rebar. Increasing the thickness of the yoke also makes sure that the flux gets concentrated more. The eventual robot can climb a wall with just one rebar located 30 mm away from it and can attain a adhesion force of 61.8N.

[22] Deep Concrete Inspection Using Unmanned Aerial Vehicle Towards CSSC Database

In this article the authors write about an automated approach for concrete spalling and crack inspection using unmanned aerial vehicles. They also try to create an open database containing concrete spalling and cracks. The goal of the writers is to locate spalling and crack regions using 3D registration and neural networks. For the database they also used pictures from the internet. The system uses visual-SLAM to build a 3D mapping system.

References

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‌[2] Renu Popli, I. Kansal, J. Verma, V. Khullar, R. Kumar, and A. Sharma, “ROAD: Robotics-Assisted Onsite Data Collection and Deep Learning Enabled Robotic Vision System for Identification of Cracks on Diverse Surfaces,” Sustainability, vol. 15, no. 12, pp. 9314–9314, Jun. 2023, https://www.mdpi.com/2071-1050/15/12/9314

[3] M. Alkhedher, Abdullah Alsit, Marah Alhalabi, Sharaf AlKheder, A. Gad, and M. Ghazal, “Novel pavement crack detection sensor using coordinated mobile robots,” Transportation Research Part C Emerging Technologies, vol. 172, pp. 105021–105021, Feb. 2025, https://www.sciencedirect.com/science/article/pii/S0968090X25000257

[4] Y. Li, H. Li, and H. Wang, “Pixel-Wise Crack Detection Using Deep Local Pattern Predictor for Robot Application,” Sensors, vol. 18, no. 9, p. 3042, Sep. 2018, https://www.mdpi.com/1424-8220/18/9/3042

[5] Md. Al-Masrur Khan, Regidestyoko Wasistha Harseno, S.-H. Kee, and Abdullah-Al Nahid, “Development of AI- and Robotics-Assisted Automated Pavement-Crack-Evaluation System,” Remote Sensing, vol. 15, no. 14, pp. 3573–3573, Jul. 2023, https://www.mdpi.com/2072-4292/15/14/3573

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[13] Parrot. (n.d.). Parrot ANAFI Ai | The 4G robotic UAV | Autonomous Photogrammetry. https://www.parrot.com/us/drones/anafi-ai/technical-documentation/photogrammetry

[14] Bridge Inspection - Infrastructure - Inspection - DJI Enterprise. (n.d.-b). DJI. https://enterprise.dji.com/inspection/bridge-inspection

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[17] Saeed, A., Erdem, M., Gurbuz, O., & Akkas, M. A. (2024). THz band drone communications with practical antennas: Performance under realistic mobility and misalignment scenarios. Ad Hoc Networks, 166, 103644. https://doi.org/10.1016/j.adhoc.2024.103644

[18] Folorunsho, S., Norris, W., (2024) Redefining Aerial Innovation: Autonomous Tethered Drones as a Solution to Battery Life and Data Latency Challenges https://arxiv.org/html/2403.07922v1

[19] Nooralishahi O, et al (2021) Drone-Based Non-Destructive Inspection of Industrial Sites: A Review and Case Studies https://www.mdpi.com/2504-446X/5/4/106

[20] Yang, L., Li, B., Feng, J., Yang, G., Chang, Y., Jiang, B., & Xiao, J. (2022). Automated wall‐climbing robot for concrete construction inspection. Journal of Field Robotics, 40(1), 110–129. https://doi.org/10.1002/rob.22119

[21] Howlader, M. D. O. F., & Sattar, T. P. (2015). Novel adhesion mechanism and design parameters for concrete wall-climbing robot. IEEE, 267–273. https://doi.org/10.1109/intellisys.2015.7361153

[22] Yang, L., Li, B., Li, W., Liu, Z., Yang, G., & Xiao, J. (2017). A robotic system towards concrete structure spalling and crack database. 2021 IEEE International Conference on Robotics and Biomimetics (ROBIO). https://doi.org/10.1109/robio.2017.8324593