PRE2023 3 Group10

Robot for saving victims in a fire

Group members
Name	Student number	Email	Study
Dimitrios Adaos	1712926	d.adaos@student.tue.nl	Computer Science and Engineering
Wiliam Dokov	1666037	w.w.dokov@student.tue.nl	Computer Science and Engineering
Kwan Wa Lam	1608681	k.w.lam@student.tue.nl	Psychology and Technology
Kamiel Muller	1825941	k.a.muller@student.tue.nl	Chemical Engineering and Chemistry
Georgi Nihrizov	1693395	g.nihrizov@student.tue.nl	Computer Science and Engineering
Twan Verhagen	1832735	t.verhagen@student.tue.nl	Computer Science and Engineering

A Victims Detection Approach for Burning Building Sites Using Convolutional Neural Networks https://ieeexplore.ieee.org/abstract/document/9031275


Title	Summary	Link
A Victims Detection Approach for Burning Building Sites Using Convolutional Neural Networks	They trained a convolutional neural network to detect people and pets in thermal IR, images. They gathered their own dataset to train the network. The network results were pretty accurate.	https://ieeexplore.ieee.org/abstract/document/9031275
Early Warning Embedded System of Dangerous Temperature Using Single exponential smoothing for Firefighters Safety	Proposes to add a temperature sensor to a firefighter's suit which will warn firefighters that they are in a very hot place > 200 C.	https://shorturl.at/bcGJ2
A method to accelerate the rescue of fire-stricken victims		https://www.sciencedirect.com/science/article/pii/S095741742302688X
The role of robots in firefighting		https://www.emerald.com/insight/content/doi/10.1108/IR-10-2020-0222/full/html
Firefighting robot with deep learning and machine vision
An autonomous firefighting robot
Real Time Victim Detection in Smoky Environments with Mobile Robot and Multi-sensor Unit Using Deep Learning