Comparative Performance of YOLOv9 and YOLOv10 for Vehicle Detection Towards Real-Time Traffic Surveillance with UAVs

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dc.contributor.authorBakirci, Murat
dc.contributor.authorBayraktar, Irem
dc.date.accessioned2025-03-17T12:22:48Z
dc.date.available2025-03-17T12:22:48Z
dc.date.issued2024
dc.departmentTarsus Üniversitesi
dc.descriptionCentro de Investigacion y de Estudios Avanzados del Instituto Politenico Nacional (Cinvestav); Centro de Investigacion y de Estudios Avanzados del Instituto Politenico Nacional (Cinvestav), Electrical Engineering Department; Electron Devices Society (EDS); IEEE
dc.description21st International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2024 -- 23 October 2024 through 25 October 2024 -- Hybrid, Mexico City -- 204854
dc.description.abstractIntelligent transportation systems (ITS) have gained significant traction since the 1980s and 1990s, driven by technological advancements and increasing urbanization, which have caused intense transportation challenges. Drone systems, with their superior imaging capabilities, offer critical solutions for swift traffic surveillance, surpassing traditional monitoring systems. In this context, object recognition is crucial, and the YOLO algorithm stands out for its speed and efficiency. This study conducts a detailed performance evaluation of the YOLOv9 and YOLOv10 networks for motor vehicle classification through aerial images captured by drone platforms. Datasets were created from these UAV-based traffic images, and the performances of both algorithms were measured and compared. The results were analyzed to highlight YOLOv9 and YOLOv10's strengths and drawbacks. Additionally, the study discusses qualitative aspects, including advantages, disadvantages, and potential improvements for both algorithms in aerial traffic monitoring. © 2024 IEEE.
dc.identifier.doi10.1109/CCE62852.2024.10771048
dc.identifier.isbn979-835037754-5
dc.identifier.scopus2-s2.0-85214882261
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/CCE62852.2024.10771048
dc.identifier.urihttps://hdl.handle.net/20.500.13099/1391
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof2024 21st International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2024
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20250316
dc.subjectaerial monitoring
dc.subjectintelligent transportation systems
dc.subjectUAV
dc.subjectvehicle detection
dc.subjectYOLOv10
dc.subjectYOLOv9
dc.titleComparative Performance of YOLOv9 and YOLOv10 for Vehicle Detection Towards Real-Time Traffic Surveillance with UAVs
dc.typeConference Object

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