Automated Helmet and Number Plate Detection Using YOLOv9: Enhancing Road Safety Compliance

Globally, motorcycle accidents remain a significant concern, often exacerbated by the failure to wear helmets. Helmets are crucial for preventing severe injuries, yet many riders choose not to use them. To tackle this issue, an automated system has been developed to detect helmet violations and identify vehicles through license plate recognition. This system leverages advanced deep learning techniques, specifically the YOLOv9 model. The YOLOv9x variant, in particular, has demonstrated exceptional performance, achieving an accuracy of 98.77%, a recall rate of 95.11%, and a mean average precision (mAP) of 96.23%. These metrics underscore the model’s precision in identifying violations. Additionally, when compared to other benchmark models such as Faster R-CNN, R-FCN, and SSD, YOLOv9 outperforms them significantly. By combining helmet detection with license plate recognition, this approach provides a scalable and efficient solution to strengthen road safety enforcement.