Vehicle Detection, Counting and Classification Using YOLOv8 Deep Learning Model and OpenCV for Urban Traffic Management System

In this paper YOLOv8 deep learning model is proposed for vehicle detection, classification, and counting for urban traffic surveillance applications on custom dataset. The model was trained with images and annotations done using the CVAT annotation tool, and it implemented in real-time using a mobile phone camera as a webcam via USB. The model accurately detected and classified four categories of vehicles such as two wheelers, three wheelers, four wheelers and large vehicles. A unique tracking ID approach was implemented to count the vehicles accurately. The obtained results prove the potential of custom model for vehicle detection and classification. The proposed model detects two wheelers, three wheelers, four wheelers and large vehicles with an accuracy of 93.6%, 86.4%, 88% and 89% respectively.