Development of an AI-Based Smart Traffic Guard System for Real-Time Detection and Automated Penalty Enforcement of Traffic Violations

Presenting the design and Development of an AI-powered Traffic Guard system aimed at detecting and penalizing common traffic violations using an integrated tech stack. The system leverages YOLOv5 for real-time object detection, particularly helmet detection for twowheeler riders. OpenCV, combined with OCR and License Plate Detection (ALPD) techniques, is utilized to extract vehicle registration numbers from live footage. Additionally, speed limit violations and red-light crossing incidents are detected using frame-based analysis and traffic signal synchronization. Once a breach is detected and verified, a digital challan is automatically generated and routed to the violator through Razorpay, enabling immediate online payment. This end-to-end solution is developed in Python, with a simple and intuitive Tkinter. GUI for monitoring and manual override, where necessary. The choice of tools ensures the system remains light, accurate, and easy to implement, even in resource-constrained environments. My focus is to maintain a balance of technical depth and realworld applicability while enhancing road safety and promoting regulatory compliance. Through this system, we aim to reduce human effort in traffic enforcement and foster a smarter and more disciplined urban transport environment.