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

• URL: http://arxiv.org/abs/2511.12206v1
• Date: Sat, 15 Nov 2025 13:18:17 GMT
• Title: A Novel AI-Driven System for Real-Time Detection of Mirror Absence, Helmet Non-Compliance, and License Plates Using YOLOv8 and OCR
• Authors: Nishant Vasantkumar Hegde, Aditi Agarwal, Minal Moharir,
• Abstract summary: Road safety is a critical global concern, with manual enforcement of helmet laws and vehicle safety standards being resource-intensive and inconsistent.<n>This paper presents an AI-powered system to automate traffic violation detection, significantly enhancing enforcement efficiency and road safety.
• Score: 0.8921166277011348
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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.

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