A Novel AI-Driven System for Real-Time Detection of Mirror Absence, Helmet Non-Compliance, and License Plates Using YOLOv8 and OCR
This provides a practical solution for automated traffic rule enforcement, addressing resource-intensive manual checks, though it is incremental as it builds on existing YOLOv8 and OCR methods.
The paper tackled automating traffic violation detection for road safety by developing an AI system that identifies helmet non-compliance, mirror absence, and license plates, achieving a precision of 0.9147 and recall of 0.886.
Road safety is a critical global concern, with manual enforcement of helmet laws and vehicle safety standards (e.g., rear-view mirror presence) being resource-intensive and inconsistent. This paper presents an AI-powered system to automate traffic violation detection, significantly enhancing enforcement efficiency and road safety. The system leverages YOLOv8 for robust object detection and EasyOCR for license plate recognition. Trained on a custom dataset of annotated images (augmented for diversity), it identifies helmet non-compliance, the absence of rear-view mirrors on motorcycles, an innovative contribution to automated checks, and extracts vehicle registration numbers. A Streamlit-based interface facilitates real-time monitoring and violation logging. Advanced image preprocessing enhances license plate recognition, particularly under challenging conditions. Based on evaluation results, the model achieves an overall precision of 0.9147, a recall of 0.886, and a mean Average Precision (mAP@50) of 0.843. The mAP@50 95 of 0.503 further indicates strong detection capability under stricter IoU thresholds. This work demonstrates a practical and effective solution for automated traffic rule enforcement, with considerations for real-world deployment discussed.