A YOLOR Based Visual Detection of Amateur Drones

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dc.contributor.authorKizilay, Emrullah
dc.contributor.authorAydin, Ilhan
dc.date.accessioned2026-08-12T16:57:33Z
dc.date.issued2022
dc.departmentFırat Üniversitesi
dc.descriptionInternational Conference on Decision Aid Sciences and Applications (DASA) -- MAR 23-25, 2022 -- Chiangrai, THAILAND
dc.description.abstractThe popularity of Unmanned Aerial Vehicles (UAV) has increased considerably among today's technologies. Especially in the field of defense technologies, it has become very important to detect UAVs in military sheltered areas, border violations or any special area that needs protection. In this study, the recently released YOLOR was used to detect UAVs. The data set created from the UAV images was trained with the YOLOR model, and as a result of the experiments, it was experimentally proven that the YOLOR model has a 95.9% success rate and is better than other object detection models..
dc.identifier.doi10.1109/DASA54658.2022.9765252
dc.identifier.endpage1449
dc.identifier.isbn978-1-6654-9501-1
dc.identifier.scopus2-s2.0-85130151927
dc.identifier.scopusqualityN/A
dc.identifier.startpage1446
dc.identifier.urihttps://doi.org/10.1109/DASA54658.2022.9765252
dc.identifier.urihttps://hdl.handle.net/11508/46496
dc.identifier.wosWOS:000839386600082
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIeee
dc.relation.ispartof2022 International Conference on Decision Aid Sciences and Applications (Dasa)
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260511
dc.subjectObject Detection
dc.subjectImage-based UAV detection
dc.subjectUAV tracking
dc.subjectDeep Learning
dc.subjectYOLOR
dc.titleA YOLOR Based Visual Detection of Amateur Drones
dc.typeConference Object

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