Enhancing Driving Assistance System with YOLO V8-Based Normal Visual Camera Sensor

One of the safety features that can alert drivers to the presence of other vehicles and reduce the risk of collisions is vehicle detection. In this study, the objective is to setup a driving support system for detecting vehicles, motorcycles, and traffic signals on the roads near to Universiti Malaysia Pahang using object detection techniques. The video was taken through a direct camera to capture video footage of traffic objects on the roads in the district, which was then analysed using the YOLO-V8 deep learning algorithm. The system was trained on a primary dataset of 1,068 images, with 70% of the dataset used for training, 20% for testing and 10% for validation. After conducting a performance validation, the system achieved a mean average precision (mAP) of 88.2% on train dataset and was able to detect different types of vehicles such as cars, motorcycles, and traffic lights. The results of this study could be beneficial for road safety authorities and researchers interested in developing intelligent transportation systems