Copyright © 2026 Authors retain the copyright of this article. This article 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.

@article{182411,
        author = {Shivam Shivkumar Shukla and Prof. Nitin Thakre and Harsh Sanjaysingh Kachhaway and Gaurav Dnyaneshwar Dupare and Tushar Marotrao Maske and Tushar Manesh Shinde},
        title = {Automating Helmet Enforcement With YOLOv9: A Deep Learning Approach},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {1876-1879},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=182411},
        abstract = {Ensuring road safety is a pressing issue globally, with a significant number of traffic fatalities involving two-wheeler riders. Non-compliance with helmet regulations remains a persistent challenge in many urban areas. This paper introduces a comprehensive and automated helmet enforcement system using YOLOv9, a state-of-the-art object detection algorithm, integrated with Optical Character Recognition (OCR) for real-time license plate detection. The system is capable of identifying helmet violations and extracting vehicle registration information through live CCTV or mobile camera feeds. Designed for scalability, accuracy, and cost-efficiency, the model minimizes manual intervention and is suitable for smart city traffic management systems. Evaluation results demonstrate high accuracy, robust performance in varied environments, and effective deployment potential.},
        keywords = {YOLOv9, Helmet Detection, Deep Learning, Traffic Monitoring, OCR, Smart Surveillance, Real-Time Object Detection},
        month = {July},
        }