AI-DRIVEN TRAFFIC MANAGEMENT SYSTEM FOR ZERO VIOLATION AND ENHANCED ROAD SAFETY

Abstract : This project addresses the critical issue of traffic safety and law enforcement, specifically focusing on noncompliance with helmet laws by motorcyclists, a leading cause of fatalities in road accidents. Traditional methods of enforcing helmet laws are often ineffective due to the challenge of ensuring immediate detection and action. To solve this, we propose an automated traffic management system that integrates artificial intelligence (AI) with real-time surveillance and data monitoring to improve road safety. The core of the proposed system utilizes YOLOv11 (You Only Look Once), a state-of-the-art object detection algorithm, for both helmet detection and license plate recognition. The system is designed to automatically identify motorcyclists riding without helmets and capture the vehicle's license plate. Once a violation is detected, the system sends an instant notification containing the license plate information to the traffic control officer’s email, enabling immediate enforcement actions. This automated approach significantly enhances the efficiency and speed of traffic law enforcement, while ensuring high accuracy through advanced deep learning techniques. With real-time image processing capabilities, the system can operate effectively in dynamic traffic conditions, ensuring prompt action and promoting compliance with traffic safety laws.

Keyword Traffic safety, law enforcement, helmet detection, license plate recognition, YOLOv11, deep learning, real-time surveillance, traffic management system, AI, automated enforcement.