Harnessing UAV Technology and YOLOv9 Algorithm for Real-Time Forest Fire Detection

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dc.contributor.authorBakirci, Murat
dc.contributor.authorBayraktar, Irem
dc.date.accessioned2025-03-17T12:22:52Z
dc.date.available2025-03-17T12:22:52Z
dc.date.issued2024
dc.departmentTarsus Üniversitesi
dc.description2024 International Russian Automation Conference, RusAutoCon 2024 -- 8 September 2024 through 14 September 2024 -- Sochi -- 203125
dc.description.abstractClimate change poses a pressing global challenge, precipitating significant natural disasters such as uncontrollable forest fires, droughts, and floods. These calamities exact severe tolls on human lives, ecosystems, and economies, prompting nations to seek proactive strategies for early forest fire intervention. While natural elements like dried vegetation and pine cones serve as primary fire catalysts, human activities exacerbate the incidence of forest fires, necessitating robust detection methods. Traditional approaches, including watchtowers and optical smoke detection systems, offer partial solutions, but real-time detection remains elusive. Unmanned Aerial Vehicles (UAVs) equipped with advanced sensors emerge as game-changers in forest fire detection, offering rapid and accurate surveillance over expansive forested areas. UAVs equipped with thermal imaging cameras detect fire heat signatures amidst dense foliage, aiding timely response efforts. Furthermore, their agility and maneuverability enable access to hazardous terrains, enhancing situational awareness and post- fire assessment. This study explores the efficacy of the YOLOv9 algorithm for real-time fire detection using UAV-captured imagery, showcasing its potential to revolutionize forest fire management. Leveraging advancements in computer vision technology, the YOLOv9 algorithm promises accelerated and precise fire detection, underscoring its pivotal role in mitigating the devastating impact of forest fires on ecosystems and communities. © 2024 IEEE.
dc.identifier.doi10.1109/RusAutoCon61949.2024.10694663
dc.identifier.endpage100
dc.identifier.issn2836-6131
dc.identifier.issue2024
dc.identifier.scopus2-s2.0-85208269380
dc.identifier.scopusqualityN/A
dc.identifier.startpage95
dc.identifier.urihttps://doi.org/10.1109/RusAutoCon61949.2024.10694663
dc.identifier.urihttps://hdl.handle.net/20.500.13099/1420
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofRusAutoCon - Proceedings of the International Russian Automation Conference
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20250316
dc.subjectaerial monitoring
dc.subjectforest fire
dc.subjectobject detection
dc.subjectUAV
dc.subjectYOLOv9
dc.titleHarnessing UAV Technology and YOLOv9 Algorithm for Real-Time Forest Fire Detection
dc.typeConference Object

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