AI TRAFFIC VIOLATIONS DETECTION SYSTEM

Abstract

The AI Traffic Violations Detection System is an intelligent surveillance solution designed to automatically detect and report traffic rule violations using a fusion of Artificial Intelligence, Machine Learning (ML), Internet of Things (IoT), Web Technologies, Mobile Application Development (MAD), and R Programming. The system identifies three critical categories of violations: helmet non-compliance among two-wheeler riders, vehicle overloading, and automatic number plate recognition (OCR) for offender identification. Real-time video feeds captured by IoT-enabled cameras are processed using computer vision and deep learning models YOLO to detect violations with high accuracy. Detected violations along with vehicle number plate data are stored in a centralized database and presented through a responsive web and mobile dashboard. R Programming is used for statistical analysis and visualization of violation trends across time, location, and vehicle type. The proposed system significantly reduces manual traffic policing effort, improves road safety compliance, and enables authorities to generate automated challans and monitor traffic patterns at scale.

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