PRE2019 3 Group5: Difference between revisions
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==Problem Statement== | ==Problem Statement== | ||
Today the technology for recycling already exists and is put to use in most first world countries, but at times it can be inefficient because of mistakes made by all of us when throwing the trash, either unwillingly or because of our ignorance. So to mitigate this and help create a better planet for the future generation, we have come up with the idea of a smart trash can, that will use a huge array of sensors and cameras to determine whether the waste should go in the glass, plastic or paper bin. This product will be small enough to fit in our homes and in the future it could be upscaled to service a full residence. | |||
==Objectives== | ==Objectives== | ||
Revision as of 22:35, 8 February 2020
Group members
| Name | Student ID | Department |
|---|---|---|
| Ana Maria Risnoveanu | 0000000 | Electrical Engineering |
| Stacey Elshove | 1279998 | Psychology and Technology |
| Petru Radulescu | 1371320 | Psychology and Technology |
| Yiqin Hou | 1281135 | Electrical Engineering |
| Tobias Hilpert | 1281070 | Chemical Engineering |
Problem Statement
Today the technology for recycling already exists and is put to use in most first world countries, but at times it can be inefficient because of mistakes made by all of us when throwing the trash, either unwillingly or because of our ignorance. So to mitigate this and help create a better planet for the future generation, we have come up with the idea of a smart trash can, that will use a huge array of sensors and cameras to determine whether the waste should go in the glass, plastic or paper bin. This product will be small enough to fit in our homes and in the future it could be upscaled to service a full residence.
Objectives
- Design an affordable smart trashcan that fits into our homes.
- Use machine learning and AI to sort the trash into their corresponding compartment.
- Utilize technologies like X-ray and image processing to achieve this.
USE Aspects
Approach, milestones and deliverables
Task distribution
Week1
| Name[total hours of work] | Tasks and hours |
|---|---|
| Ana Maria Risnoveanu | ... |
| Stacey Elshove | ... |
| Petru Radulescu | Objectives ... |
| Yiqin Hou[?h] | Papers about X-ray imaging[1][2][3][4]/Object recognition[5] searched and studied[?h]; summary[?h]; Learn wikitext and make template for the group wiki[4h] |
| Tobias Hilpert | ... |
SotA
Detection
X-ray
Inspired by security machines, X-ray imaging could be a usefull tool in detcting the type of rubbish. X-ray imaging [3]makes use of the property of X-ray that it attenuates differently accorss difderent materials. For example metal atoms and ions attenuate more X-ray than normal organic tissue, such as fat and protein. Some new X-ray imaging techniques could even determine the chemical structures that form within the materials[4]. With such techiniques and some morden X-ray imaging detectors [2], the bin can distinguish materials much more accurate than using normal X-ray imaging. It is also possible to distinguish different types of plastics with X-ray imaging[1], which further increases the recycling sorting process.
Despite its reliable performance in detecting metal and different kinds of organic materials, an X-ray imaging system is too expensive to implement in a trash bin. Even the cheapest X-ray tubes cost $100 to $500 each, let alone the detectors, power supply and other systems.
Computer vision and image processing
Object recognition [5]
References
- ↑ 1.0 1.1 Peco-InspX. Detecting Plastics with X-Ray Inspection Systems. Date accessed: 2020-02-05. https://www.peco-inspx.com/blog/x-ray-detectable-plastics/
- ↑ 2.0 2.1 Sol M. Gruner.(2012). "X-ray imaging detectors" https://physicstoday.scitation.org/doi/10.1063/PT.3.1819 Pulisher: American Institute of Physics. Date accessed: 2020-02-06
- ↑ 3.0 3.1 Mary E.Coles. (1999). "8. X-Ray Imaging". https://doi.org/10.1016/S0076-695X(08)60419-6 . 35: 301-336. Date accessed: 2020-02-08.
- ↑ 4.0 4.1 Schroer, Christian G. (2011). "X-ray imaging: The chemistry inside". Nature. 476: 159-160. https://www-nature-com.dianus.libr.tue.nl/articles/476159a Date accessed: 2020-02-06
- ↑ 5.0 5.1 Liu, Ying-Ho; Lee, Anthony J.T.; Chang, Fu. (2012). "Object recognition using discriminative parts". Computer Vision and Image Understanding. 116: 854-867.