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Title: Détection D'incendie Et De Fumée A L'aide De L'apprentissage Par Transfert (Fire and Smoke Detection Using Transfer Learning
Authors: LATI, Abdelhai
BEKKARI, Lakhdar
KADI, Nadhir
Keywords: Fire and Smoke Detection
Transfer Learning
Lightweight Networks
Computer Vision
Issue Date: 2022
Abstract: The past decade has seen tremendous growth in the fields of artificial intelligence and computer vision. Recent advancements in several technologies have enabled the growth and development of intelligent surveillance systems through the development of advanced computing and data transmission over the Internet. A fire and smoke detection system requires accurate, fast and real-time response mechanisms to make the right decision and immediately notify the corresponding personnel. Common fire detection systems cannot be used outdoors and do not guarantee the necessary availability for real-time detection. The objective of this project is to use a Deep Neural Network to detect fire and smoke in outdoor environments using an embedded system with a camera. Recently, after significant progress in programming structures and, above all, in the field of machine learning, embedded systems are now able to perform real-time edge computing. This project proposes an approach to perform computer vision fire and smoke detection using advanced computing. Once the fire or smoke appears in any camera, the system can detect and immediately send an alert to the corresponding supervisor
Description: Électronique Des Systèmes Embarqués
Appears in Collections:Département d'Electronique et des Télécommunications - Master

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