Abstract

Object detection plays a crucial role in the development of intelligent and efficient traffic management systems, allowing authorities to effectively monitor, analyze, and regulate traffic flow. This paper introduces the design and implementation of a real-time object detection system powered by YOLOv8 (You Only Look Once), a cutting-edge deep learning model recognized for its high speed and accuracy in object detection tasks. The proposed system can identify and classify various vehicle types—such as cars, trucks, buses, motorcycl bicycles—from live traffic surveillance footage. Experimental results indicate that YOLOv8 delivers high detection accuracy alongside real-time processing, making it highly viable for practical deployment in traffic monitoring and law enforcement applications. By integrating object detection with speed and surveillance features, the system offers a holistic solution to modern traffic management issues. This research underscores the potential of YOLOv8-based systems in supporting automated and Intelligent Transportation Systems (ITS), contributing to safer and more efficient urban mobility. Future enhancements may involve incorporating multi object tracking (MOT) for persistent vehicle tracking and extending the system’s functionality to operate under nighttime or low-visibility conditions.