UAV aerial image-based forest fire detection using deep learning

dc.contributor.author	Keddous, Akila; Lagha, Mohand (promoteur); Choutri, Kheireddine (promoteur)
dc.date.accessioned	2021-12-06T10:21:12Z
dc.date.available	2021-12-06T10:21:12Z
dc.date.issued	2021
dc.identifier.uri	http://di.univ-blida.dz:8080/jspui/handle/123456789/13403
dc.description	Mémoire de Master option Avionique.-Numéro de Thèse 054/2021	fr_FR
dc.description.abstract	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).	fr_FR
dc.language.iso	en	fr_FR
dc.publisher	Université Blida 01	fr_FR
dc.subject	Real-time fire detection; Deep learning; Convolutional neural network; Computer vision; Unmanned aerial vehicles; Fire Datasets; YOLOv2	fr_FR
dc.title	UAV aerial image-based forest fire detection using deep learning	fr_FR
dc.type	Thesis	fr_FR