Traffic Eye: AI-Powered Traffic Rules Violation Detection and Management

Abstract

The Traffic Eye: AI-Powered Traffic Rules Violation Detection and Management System is developed to support efficient traffic monitoring and improve road safety through image-based analysis. The system processes captured traffic images to detect vehicles and identify common violations such as helmetless riding, mobile phone usage while driving, stunt riding, wrong-side movement, and one-way violations. YOLOv8 is used to detect relevant objects including vehicles, riders, helmets, and mobile phones from traffic scenes. Spatial relationships between these detected objects are analyse to determine violation occurrences. To associate violations with specific vehicles, Easy OCR extracts registration numbers from detected license plates. The violation type is manually confirmed by the user along with visual evidence to ensure reliability. The system also provides voice alerts and automated email notifications for effective communication. A simple user interface allows traffic authorities to review violations and generate reports. Overall, the system offers a practical and intelligent solution for automated traffic rule enforcement.

Introduction

The text presents an AI-based traffic rule violation detection and management system, called Traffic Eye, designed to address the growing challenges of traffic regulation and road safety caused by increasing vehicle density. Traditional manual surveillance methods are inefficient, limited in coverage, and prone to human error, often resulting in delayed or missed enforcement. Advances in artificial intelligence and computer vision enable automated systems that analyze traffic images and videos in real time with higher accuracy and reliability.

The proposed system uses YOLOv8 for real-time object detection to identify vehicles, riders, helmets, and their spatial relationships, enabling the detection of violations such as helmetless riding, mobile phone use, stunt riding, wrong-side driving, signal violations, and overloading. EasyOCR is integrated for accurate license plate recognition, allowing automated offender identification and record generation. The system also includes adaptive processing to handle low-light and varying weather conditions.

Traffic Eye follows a modular client–server architecture with a “Detection–Validation–Action” workflow to reduce false positives. Confirmed violations trigger voice alerts using pyttsx3 and email notifications with image evidence via SMTP, ensuring timely communication with authorities.

Experimental evaluation using real-world traffic images and video frames demonstrates strong performance, achieving 93.5% accuracy, 91.8% precision, 90.6% recall, and an F1-score of 91.2%. The system operates efficiently on standard hardware and performs consistently across diverse traffic and environmental conditions. Overall, the study concludes that the AI-driven Traffic Eye system provides a scalable, accurate, and practical solution for modern traffic monitoring and enforcement.

Conclusion

This Project successfully demonstrates how artificial intelligence and computer vision can be applied to improve traffic monitoring and enforcement in a practical and efficient manner. By using a YOLOv8-based detection model along with Easy OCR for number plate recognition, the system is able to automatically identify common traffic violations and associate them with the correct vehicles. The integration of automated email alerts and voice notifications further strengthens the system by enabling quick communication and real-time awareness. Overall, the solution reduces dependence on manual surveillance, minimizes human error, and provides reliable digital evidence for enforcement. The results show that such an intelligent system can play a significant role in enhancing road safety, promoting disciplined driving behaviour, and supporting smarter traffic management in modern cities

References

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Copyright

Copyright © 2025 Bhavana M K, Mr. Sathyanarayana S, Bhavana B M, G N Nisarga, Harshitha S. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.