Published Paper Details:

In recent years, there has been a steady rise in the number of road accidents in India, resulting in a rise in injuries and fatalities. Particularly, the failure of motorcyclists to wear helmets is one of the significant contributors to severe injuries and casualties in road accidents. The rise in population and the growing number of motorcyclists on the road have made police patrolling challenging and labor-intensive. The proposed system aims to address this issue by developing a specialized computing model designed to identify motorcyclists not wearing helmets. Upon successful detection, the system promptly retrieves the vehicle's number plate and transmits this vital information to relevant authorities for necessary action. This involves issuing an automated challan to the violator and stating the reason for the fine. The application utilizes the YOLO (You Only Look Once) computer vision algorithm, known for its efficiency in real-time object detection and classification. Recently, a new version of the YOLOv8 algorithm has been developed by Ultralytics, taking into account the limitations of its predecessor models. With a focus on speed, accuracy, and user-friendly design, YOLOv8 is a robust choice for various computer vision tasks. Its capabilities extend to object detection and tracking, instance segmentation, and image classification, demonstrating a flexible and high-performance solution for diverse applications. This integration of technology tackles the critical issue of helmetless driving, thereby promoting road safety by enhancing stricter measures. By automating the identification and penalty processes, the proposed system aims to optimize enforcement efforts, ensuring a safer environment for all commuters.