Comparative Performance Analysis of YOLOv5 and YOLOv8 for Aerial Surveillance in Smart Traffic Management

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dc.contributor.authorNagpal, Sujal
dc.contributor.authorEtnubarhi, Nahom
dc.contributor.authorPirehen, Hale Filiz
dc.contributor.authorBayraktar, Irem
dc.date.accessioned2025-03-17T12:22:47Z
dc.date.available2025-03-17T12:22:47Z
dc.date.issued2024
dc.departmentTarsus Üniversitesi
dc.descriptionIEEE SMC; IEEE Turkiye Section
dc.description2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024 -- 16 October 2024 through 18 October 2024 -- Ankara -- 204562
dc.description.abstractDetection and classification of vehicles in various traffic conditions are critical to advances in traffic monitoring and autonomous vehicle technologies. This study addresses the need for more accurate and efficient vehicle detection algorithms by evaluating the performance of YOLOv5 and YOLOv8 object detection models. The smallest and largest submodels (YOLOv5s, YOLOv5x, YOLOv8s, and YOLOv8x) were tested using images captured under extreme conditions, including nighttime and high-altitude drone images. The methodology involves systematically comparing the detection accuracy and inference speed of these models. The results revealed that the 'x' submodels outperformed the 's' submodels in terms of detection accuracy across various scenarios. In particular, YOLOv8x demonstrated high detection and classification performance in complex conditions, such as images with varying light filters and dark backgrounds. Conversely, the 's' submodels, although significantly faster, exhibited lower detection accuracy. Additionally, YOLOv8 models outperformed YOLOv5 models across all performance metrics, with YOLOv8x providing the highest accuracy. The study concludes that while there is a trade-off between detection accuracy and inference speed, the YOLOv8x model offers the best balance for applications that require high precision in vehicle detection. These findings provide valuable insights into selecting the appropriate model according to specific application requirements, ultimately contributing to the development of traffic monitoring and autonomous vehicle systems. © 2024 IEEE.
dc.identifier.doi10.1109/ASYU62119.2024.10757062
dc.identifier.isbn979-835037943-3
dc.identifier.scopus2-s2.0-85213400998
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/ASYU62119.2024.10757062
dc.identifier.urihttps://hdl.handle.net/20.500.13099/1387
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof2024 Innovations in Intelligent Systems and Applications Conference, ASYU 2024
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20250316
dc.subjectdeep learning
dc.subjectdrone-based monitoring
dc.subjectintelligent transportation systems
dc.subjectvehicle detection
dc.subjectYOLO
dc.titleComparative Performance Analysis of YOLOv5 and YOLOv8 for Aerial Surveillance in Smart Traffic Management
dc.typeConference Object

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