Smart Traffic Surveillance: Helmet Detection and License Plate Recognition Using Deep Learning

Motorcyclists are particularly vulnerable to road accidents, often resulting in severe injuries or fatalities. Helmets are a proven safety measure that significantly reduces the risk of head injuries; however, many riders fail to comply with helmet laws, making enforcement a challenge for traffic authorities. This paper presents an advanced, automated traffic violation detection system that integrates real-time helmet detection and automatic license plate recognition to enhance road safety and streamline enforcement. Our system employs an optimized YOLOv3 model to efficiently detect motorcycles and determine whether riders are wearing helmets. Unlike conventional implementations, we enhance detection accuracy by fine-hyperparameter tuning YOLOv3 on a diverse dataset that includes various helmet types, different lighting conditions, and occlusions. To address challenges such as low-light environments, we incorporate preprocessing techniques, including contrast enhancement and adaptive thresholding, improving detection performance under suboptimal conditions. For license plate recognition, we utilize EasyOCR, further improved through custom preprocessing steps such as noise reduction and edge enhancement and thresholding enabling better recognition of partially occluded or low-quality license plates. Upon detecting a violation, the system automatically extracts and logs the license plate details into a structured database, facilitating streamlined enforcement and legal action. Our experimental results demonstrate increased accuracy while reducing inference time compared to existing methods, making this system a scalable and deployable solution for real-time traffic monitoring. By automating violation detection and reporting, the proposed approach reduces the burden on law enforcement while encouraging greater compliance with helmet laws, ultimately contributing to safer roads.