Real-Time Vehicle Detection Using YOLOv8-Nano for Intelligent Transportation Systems

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dc.contributor.authorBakirci, Murat
dc.date.accessioned2025-03-17T12:25:24Z
dc.date.available2025-03-17T12:25:24Z
dc.date.issued2024
dc.departmentTarsus Üniversitesi
dc.description.abstractIn recent years, deep learning models have seen extensive use in various domains, with the YOLO algorithm family emerging as a prominent player. YOLOv5, known for its real-time object detection capabilities and high accuracy, has been widely embraced in transportation- related research. However, the introduction of YOLOv8 in early 2023 signifies a significant leap forward in object detection technology. Despite its potential, the literature on YOLOv8 remains relatively scarce, leaving room for exploration and adoption in research. This study pioneers real-time vehicle detection using the YOLOv8 algorithm. An in-depth analysis of YOLOv8n, the smallest scale model within the YOLOv8 series, was conducted to assess its suitability for real-time scenarios, particularly in Intelligent Transportation Systems (ITS). To reinforce its real-time capabilities, a parametric analysis covering image processing time, detection sensitivity, and input image characteristics was performed. To optimize model performance, a training dataset was created through flight tests using a custom autonomous drone, encompassing various vehicle variations. This ensures that the model excels in recognizing diverse motor vehicle configurations. The results reveal that even this compact sub-model achieves an impressive detection accuracy rate exceeding 80%. The study establishes that YOLOv8n, evaluated for the first time in ITS applications, effectively serves as an object detector for real-time smart traffic management.
dc.identifier.doi10.18280/ts.410407
dc.identifier.endpage1740
dc.identifier.issn0765-0019
dc.identifier.issn1958-5608
dc.identifier.issue4
dc.identifier.scopusqualityN/A
dc.identifier.startpage1727
dc.identifier.urihttps://doi.org/10.18280/ts.410407
dc.identifier.urihttps://hdl.handle.net/20.500.13099/1664
dc.identifier.volume41
dc.identifier.wosWOS:001315425300007
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.institutionauthorBakirci, Murat
dc.language.isoen
dc.publisherInt Information & Engineering Technology Assoc
dc.relation.ispartofTraitement Du Signal
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250316
dc.subjectvehicle detection
dc.subjectYOLOv8
dc.subjectaerial monitoring
dc.subjectintelligent transportation systems
dc.subjectUAV
dc.titleReal-Time Vehicle Detection Using YOLOv8-Nano for Intelligent Transportation Systems
dc.typeArticle

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