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

In 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 transportationrelated 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.