AI-Driven Vehicle Detection and Counting System Using Yolov5

Vehicle flow analysis and traffic monitoring are crucial elements of contemporary intelligent transportation systems. Traditional manual traffic monitoring methods are inefficient, time-consuming, and prone to human error. Recent advancements in computer vision and deep learning have enabled the development of automated solutions capable of detecting and analyzing vehicles in real time using video data. In particular, deep neural network-based object detection models have demonstrated significant improvements in accuracy and speed. This study presents an AI-driven vehicle detection and counting system using the YOLOv5 (You Only Look Once version 5) object detection model. The proposed system processes video streams captured from surveillance cameras and automatically identifies multiple vehicle types, such as cars, buses, trucks, and motorcycles. By leveraging the real-time capabilities of YOLOv5, the system detects vehicles in each frame and applies a tracking and counting mechanism to determine the number of vehicles passing through a defined region. This implementation integrates deep learning–based object detection with computer vision techniques to provide efficient traffic monitoring. The system was trained on labeled datasets containing various traffic scenarios and evaluated using metrics such as precision, recall, and mean Average Precision (mAP). The experimental results demonstrate that the proposed model achieves reliable detection performance and enables accurate vehicle counting, even in dynamic traffic environments. This study contributes to the development of intelligent traffic management solutions by providing an automated, scalable, and cost-effective system for vehicle detection and counting. The proposed approach can support applications such as smart city infrastructure, traffic congestion analysis, road safety monitoring and transportation planning.