Multi-Class Vehicle Detection and Classification with YOLO11 on UAV-Captured Aerial Imagery

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dc.contributor.authorBakirci, Murat
dc.contributor.authorDmytrovych, Petro
dc.contributor.authorBayraktar, Irem
dc.contributor.authorAnatoliyovych, Oleh
dc.date.accessioned2025-03-17T12:22:47Z
dc.date.available2025-03-17T12:22:47Z
dc.date.issued2024
dc.departmentTarsus Üniversitesi
dc.description7th IEEE International Conference on Actual Problems of Unmanned Aerial Vehicles Development, APUAVD 2024 -- 22 October 2024 through 24 October 2024 -- Kyiv -- 204640
dc.description.abstractAerial imaging and object detection using unmanned aerial vehicle (UAV) systems pose unique challenges, including varying altitudes, dynamic backgrounds, and changes in lighting and weather conditions. These factors complicate the detection process, demanding robust and adaptive algorithms. Furthermore, the need for real-time processing in UAV applications imposes stringent requirements on computational efficiency and resource management. This study presents a comparative analysis of the cutting-edge object detection algorithm YOLO11, specifically tailored for vehicle detection in UAV-captured traffic images. Using a custom dataset derived from UAV aerial imaging, the algorithm was trained and evaluated to assess both its performance in terms of speed and accuracy, and the results were compared with YOLOv10. Experimental findings indicate that while YOLOv10 achieves slightly faster inference speeds, YOLO11 offers marginally better detection accuracy. © 2024 IEEE.
dc.identifier.doi10.1109/APUAVD64488.2024.10765862
dc.identifier.endpage196
dc.identifier.isbn979-833153414-1
dc.identifier.scopus2-s2.0-85213084782
dc.identifier.scopusqualityN/A
dc.identifier.startpage191
dc.identifier.urihttps://doi.org/10.1109/APUAVD64488.2024.10765862
dc.identifier.urihttps://hdl.handle.net/20.500.13099/1381
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof2024 IEEE 7th International Conference on Actual Problems of Unmanned Aerial Vehicles Development, APUAVD 2024 - Proceedings
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20250316
dc.subjectintelligent transportation systems
dc.subjectunmanned aerial vehicle
dc.subjectvehicle detection and classification
dc.subjectYOLO11
dc.subjectYOLOv10
dc.titleMulti-Class Vehicle Detection and Classification with YOLO11 on UAV-Captured Aerial Imagery
dc.typeConference Object

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