<![CDATA[An automatic vehicle detection and counting system is essential for Intelligent Transportation Systems (ITS) to monitor and manage traffic effectively. This study evaluates the performance of the lightweight YOLOv8n (nano) model for vehicle detection and classification, combined with a Centroid Tracking algorithm to improve vehicle counting accuracy. YOLOv8n was selected for its balance between computational efficiency and detection accuracy, making it suitable for devices with limited resources. The research involved collecting a dataset of seven vehicle classes (bus_l, bus_s, car, truck_l, truck_m, truck_s, truck_xl), followed by data preprocessing and training the YOLOv8n model for 40 epochs. Data augmentation techniques were applied to enhance data variability and improve model robustness. The Centroid Tracking algorithm was integrated to maintain vehicle identity across frames and prevent double counting. Model evaluation used precision, recall, F1-score, and mean Average Precision (mAP). Results show YOLOv8n achieved an overall mAP@0.5 of 0.820. The “car” class attained the highest mAP of 0.963, while “truck_s” had the lowest at 0.665, mainly due to imbalanced data distribution. The Centroid Tracking effectively maintained object identities and provided consistent vehicle counts during testing. This combination offers a reliable and efficient system suitable for real-time traffic monitoring, parking management, and enhancing road safety. The YOLOv8n and Centroid Tracking-based system demonstrates strong potential for practical ITS applications, especially on devices with limited computational resources. Future work should focus on expanding the dataset and improving class balance to further enhance detection accuracy and system robustness.]]>
