Citation:

Ms. Muthulakshmi P, Dr. Vinukumar K, Mr. Rakesh S, 2026. "Image Based Helmet Detection Using Deep Neural Networks", ESP Journal of Engineering & Technology Advancements  6(1): 167-174.

Abstract:

In recent years, road safety has become a significant concern due to the rise in traffic violations and accidents. One of the major causes of fatalities is the non- compliance with helmet usage among two-wheeler riders. This project proposes an AI-powered system for automatic helmet detection to ensure traffic law compliance. The system uses Deep Neural Network (DNN) YOLOv9 models to accurately detect helmet usage. Once a violation is detected—such as a rider without a helmet—the number plate is extracted and processed using a deep learning-based character recognition model. The system operates entirely through an AI-driven software pipeline that analyzes image or video input in real time. It detects riders, verifies helmet usage, localizes number plates, and recognizes registration details. Upon detecting a violation, the system can log the event, store evidence, and trigger automated digital notifications to relevant authorities. This software-based automation minimizes manual effort, enhances detection accuracy, and reduces delays in addressing traffic violations. The proposed solution is scalable and adaptable for deployment in high-traffic environments, contributing significantly to improved road safety and more effective enforcement of traffic regulations.

References:

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Keywords:

Helmet detection, Number plate recognition, Deep learning, Road safety, Traffic monitoring.