Challenges and Advances in UAV-Based Vehicle Detection Using YOLOv9 and YOLOv10

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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 with unmanned aerial vehicle (UAV) systems present 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 two cutting-edge object detection algorithms, YOLOv9 and YOLOv10, specifically tailored for vehicle detection in UAVcaptured traffic images. Leveraging a custom dataset derived from UAV aerial imaging, both algorithms were trained and evaluated to assess their performance in terms of speed and accuracy. The experimental results reveal that while YOLOv9 demonstrates a marginally superior inference speed, YOLOv10 excels slightly in detection accuracy. © 2024 IEEE.
dc.identifier.doi10.1109/APUAVD64488.2024.10765874
dc.identifier.endpage321
dc.identifier.isbn979-833153414-1
dc.identifier.scopus2-s2.0-85213362533
dc.identifier.scopusqualityN/A
dc.identifier.startpage317
dc.identifier.urihttps://doi.org/10.1109/APUAVD64488.2024.10765874
dc.identifier.urihttps://hdl.handle.net/20.500.13099/1382
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.subjectobject detection
dc.subjecttraffic management
dc.subjectunmanned aerial vehicle
dc.subjectvehicle classifiction
dc.subjectYOLOv10
dc.subjectYOLOv9
dc.titleChallenges and Advances in UAV-Based Vehicle Detection Using YOLOv9 and YOLOv10
dc.typeConference Object

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