Development of a deep learning-based surveillance system for forest fire detection and monitoring using UAV

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dc.authoridDEMIR, Batikan Erdem/0000-0001-6400-1510
dc.authoridSHAMTA, IBRAHIM/0009-0003-1280-679X
dc.contributor.authorShamta, Ibrahim
dc.contributor.authorDemir, Batikan Erdem
dc.date.accessioned2024-09-29T16:06:08Z
dc.date.available2024-09-29T16:06:08Z
dc.date.issued2024
dc.departmentKarabük Üniversitesien_US
dc.description.abstractThis study presents a surveillance system developed for early detection of forest fires. Deep learning is utilized for aerial detection of fires using images obtained from a camera mounted on a designed four-rotor Unmanned Aerial Vehicle (UAV). The object detection performance of YOLOv8 and YOLOv5 was examined for identifying forest fires, and a CNN-RCNN network was constructed to classify images as containing fire or not. Additionally, this classification approach was compared with the YOLOv8 classification. Onboard NVIDIA Jetson Nano, an embedded artificial intelligence computer, is used as hardware for real-time forest fire detection. Also, a ground station interface was developed to receive and display fire-related data. Thus, access to fire images and coordinate information was provided for targeted intervention in case of a fire. The UAV autonomously monitored the designated area and captured images continuously. Embedded deep learning algorithms on the Nano board enable the UAV to detect forest fires within its operational area. The detection methods produced the following results: 96% accuracy for YOLOv8 classification, 89% accuracy for YOLOv8n object detection, 96% accuracy for CNN-RCNN classification, and 89% accuracy for YOLOv5n object detection.en_US
dc.identifier.doi10.1371/journal.pone.0299058
dc.identifier.issn1932-6203
dc.identifier.issue3en_US
dc.identifier.pmid38470887en_US
dc.identifier.scopus2-s2.0-85187577880en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1371/journal.pone.0299058
dc.identifier.urihttps://hdl.handle.net/20.500.14619/6637
dc.identifier.volume19en_US
dc.identifier.wosWOS:001192362300005en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherPublic Library Scienceen_US
dc.relation.ispartofPlos Oneen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectWrky Transcription Factorsen_US
dc.subjectDrought Stressen_US
dc.subjectRna-Seqen_US
dc.subjectAbiotic Stressen_US
dc.subjectToleranceen_US
dc.subjectSalten_US
dc.subjectDehydrationen_US
dc.subjectExpressionen_US
dc.subjectGenotypesen_US
dc.subjectResponsesen_US
dc.titleDevelopment of a deep learning-based surveillance system for forest fire detection and monitoring using UAVen_US
dc.typeArticleen_US

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