Traffic Rules Violation Detection System

Abstract

Road traffic accidents and violations represent one of the most critical global public health challenges, causing approximately 1.3 million deaths annually according to the WHO.[25] Traffic enforcement, particularly for helmet usage and lane discipline, remains largely manual and inconsistent across developing nations. This paper presents a comprehensive software centric framework for automatic traffic rule violation detection and fine collection[1] using state-of-the-art deep learning models (YOLOv5/YOLOv8) [4],[5]integrated with optical character recognition (OCR)[7] and automated fine generation systems. The proposed system processes video streams from traffic cameras, simultaneously detecting helmet violations, identifying riders, recognizing license plates through multi-model inference, and dispatching automated challen (traffic fines) to violators via email. We evaluate the framework on real world traffic footage, achieving 94.3% helmet detection accuracy, 89.7% license plate recognition accuracy, and sub-second inference latency across multiple frame rates. The system operates on commodity hardware (CPU/GPU) and is production-ready for deployment on existing traffic monitoring infrastructure. This work addresses the critical gap between detection technology and penalty enforcement, offering municipalities and traffic authorities a scalable, transparent, and data driven approach to road safety management. Results demonstrate that end-to-end automation from violation detection to fine collection can be achieved with 96.2% effectiveness, significantly reducing administrative overhead while maintaining legal compliance.

Keywords