A Novel AI-Driven System for Real-Time Detection of Mirror Absence, Helmet Non-Compliance, and License Plates Using YOLOv8 and OCR

TL;DR

This paper introduces an AI system combining YOLOv8 and OCR to automate detection of traffic violations like helmet non-compliance, mirror absence, and license plate recognition, improving enforcement efficiency and road safety.

Contribution

It presents an innovative AI-powered system that integrates object detection and OCR for real-time traffic violation detection, including a novel check for rear-view mirror presence.

Findings

Achieved 0.9147 precision and 0.886 recall in detection tasks.

Demonstrated effective license plate recognition under challenging conditions.

Provided a practical deployment framework with a user-friendly interface.

Abstract

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.