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This paper introduces an AI-driven system that leverages YOLOv5m for object and violation detection and employs a Convolutional Neural Network (CNN) for accurate license plate classification. The system processes recorded video footage to identify high-risk driving behaviors, such as helmet non-compliance, mobile phone usage, and seatbelt violations. It also classifies license plates into categories (public, government, foreign, military) with high accuracy. Integrated into a user-friendly web interface, the system enables detailed analysis of recorded videos and data export for reporting purposes. Tailored to the Tunisian context, this innovation addresses critical road safety challenges with scalable, data-driven policy enhancements.

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AI-Driven System for Detecting High-Risk Traffic Violations Using YOLOv5m and CNN

  • 会议论文

  • 发表日期：

    2026年

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  • Hassen Ben Rebah

摘要

This paper introduces an AI-driven system that leverages YOLOv5m for object and violation detection and employs a Convolutional Neural Network (CNN) for accurate license plate classification. The system processes recorded video footage to identify high-risk driving behaviors, such as helmet non-compliance, mobile phone usage, and seatbelt violations. It also classifies license plates into categories (public, government, foreign, military) with high accuracy. Integrated into a user-friendly web interface, the system enables detailed analysis of recorded videos and data export for reporting purposes. Tailored to the Tunisian context, this innovation addresses critical road safety challenges with scalable, data-driven policy enhancements.