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dc.contributor.authorKeddous, Akila; Lagha, Mohand (promoteur); Choutri, Kheireddine (promoteur)
dc.date.accessioned2021-12-06T10:21:12Z
dc.date.available2021-12-06T10:21:12Z
dc.date.issued2021
dc.identifier.urihttp://di.univ-blida.dz:8080/jspui/handle/123456789/13403
dc.descriptionMémoire de Master option Avionique.-Numéro de Thèse 054/2021fr_FR
dc.description.abstractForest 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.isoenfr_FR
dc.publisherUniversité Blida 01fr_FR
dc.subjectReal-time fire detection; Deep learning; Convolutional neural network; Computer vision; Unmanned aerial vehicles; Fire Datasets; YOLOv2fr_FR
dc.titleUAV aerial image-based forest fire detection using deep learningfr_FR
dc.typeThesisfr_FR
Appears in Collections:Mémoires de Master

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