Veuillez utiliser cette adresse pour citer ce document : https://di.univ-blida.dz/jspui/handle/123456789/13403
Titre: UAV Aerial Image-Based Forest Fire Detection Using Deep Learning
Auteur(s): Keddous, Akila; LAGHA, Mohand (promoteur); CHOUTRI, K. E. (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: 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: 054/021 Option: Avionique Promoteur :Dr. Lagha Mohand ; Dr. Choutri Kheireddine
URI/URL: http://di.univ-blida.dz:8080/jspui/handle/123456789/13403
Collection(s) :Mémoires de Master

Fichier(s) constituant ce document :
Fichier Description TailleFormat 
UAV Aerial Image-Based Forest Fire Detection Using Deep Learning KEDDOUS AKILA 2.0.pdf4,55 MBAdobe PDFVoir/Ouvrir


Tous les documents dans DSpace sont protégés par copyright, avec tous droits réservés.