UAV aerial image-based forest fire detection using deep learning

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Titre:	UAV aerial image-based forest fire detection using deep learning
Auteur(s):	Keddous, Akila; Lagha, Mohand (promoteur); Choutri, Kheireddine (promoteur)
Mots-clés:	Real-time fire detection; Deep learning; Convolutional neural network; Computer vision; Unmanned aerial vehicles; Fire Datasets; YOLOv2
Date de publication:	2021
Editeur:	Université Blida 01
Résumé:	Forest fires are very dangerous. Once they become widespread, it is very difficult to extinguish. In this work, an Unmanned aerial vehicle (UAV) image-based Real-time Forest fire detectionapproach is proposed. Where we took advantage of recent development in computer vision systems and the rapid maneuverability of Unmanned Aerial Vehicles to improve the performance of the Real time detection, we designed and implemented a YOLOv2Convolutional Neural Network Model in MATLAB to train on an aerial dataset, Experimentalresults show that our proposed system has high detection performance, and its detection speedreaches 58 Frame Per Second with a mean average precision of 0.87, thereby satisfying therequirements of real-time detection (Speed and Accuracy).
Description:	Mémoire de Master option Avionique.-Numéro de Thèse 054/2021
URI/URL:	http://di.univ-blida.dz:8080/jspui/handle/123456789/13403
Collection(s) :	Mémoires de Master

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Fichier	Description	Taille	Format
UAV Aerial Image-Based Forest Fire Detection Using Deep Learning KEDDOUS AKILA 2.0.pdf		4,55 MB	Adobe PDF	Voir/Ouvrir

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