Real-Time Vehicle Detection and Classification in Traffic Videos Using Yolov8

Abstract

The Vehicle detection is important for the enhancement of transportation systems and which is efficient for traffic management, improved road safety and accurate data collection by automatically identifying and tracking vehicles on roads which enables features like traffic signal optimization, speed measurement and accident detection ultimately contributing to a smoother and safer driving experience for everyone. Here we have built a real-time project which can detect car, bus, motorcycle and truck on the basis of algorithm called YOLOV8 (You Only Look Once Version 8). It is a computer vision technique which is renowned for its real-time object detection capabilities, providing optimal balance between speed and accuracy. The model is trained using PyTorch and leverages Convolutional Neural Network (CNN) for feature extraction.