Bakirci, Murat2025-03-172025-03-1720240765-00191958-5608https://doi.org/10.18280/ts.410407https://hdl.handle.net/20.500.13099/1664In 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.eninfo:eu-repo/semantics/closedAccessvehicle detectionYOLOv8aerial monitoringintelligent transportation systemsUAVArticle10.18280/ts.41040741417271740Q4WOS:001315425300007N/A