Automated Traffic Violation Detection Using Deep Learning and Computer Vision
Keywords:
Computer vision, Convolutional neural networks, Deep learning, Helmet detection, Object detection, Road safety, Traffic violation detection, Triple riding, YOLO, YOLOv8Abstract
Road traffic violations such as triple riding on two-wheelers and failure to wear helmets are among the leading contributors to accident fatalities in India. Traditional enforcement methods relying on human observation are prone to error, inconsistency, and scale limitations. This paper presents a real-time automated traffic violation detection system that leverages deep learning and computer vision techniques to identify and report such violations from road imagery. The proposed solution employs a two-stage detection pipeline built on the YOLOv8s architecture. The first stage detects motorcycles, triple-riding instances, helmet status, and number plates simultaneously. The second stage performs targeted helmet verification on individual rider crops using a dedicated fine-tuned helmet model, reducing false positives for ambiguous cases. Experimental results demonstrate that the v2 system achieves precision of 82.7%, recall of 74.4%, and mAP@50 of 71.6% on the triple ride model, representing improvements of over 81 percentage points in precision compared to the heuristic-based v1 baseline. The helmet model achieves mAP@50 of 80.8% and precision of 81.2%. The complete pipeline operates at approximately 58 frames per second on a Tesla T4 GPU, making it suitable for real-time deployment. A Gradio-based web interface was developed to provide a user-friendly demonstration and inference platform.
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