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@article{178652,
        author = {Mr.G.Venkata Subba Rao and A.Srikanth Reddy and D.Rajeshwar Reddy and A.Shivanand},
        title = {Helmet and Number Plate Detection Using Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {4367-4371},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178652},
        abstract = {The rising number of road traffic accidents, especially involving two-wheelers, highlights the urgent need for effective road safety measures. Among the key contributors to road fatalities are the non-compliance with helmet laws and the difficulty in identifying violators due to unclear or missing vehicle number plates. This paper presents an advanced, real-time helmet and motorcycle detection system that integrates the latest YOLOv9 object detection model with Easy OCR for robust number plate recognition. YOLOv9 significantly improves detection accuracy for motorcycles and helmet usage, even under challenging conditions such as low light and crowded traffic scenarios. Once the number plate is detected, the system cross-references it with a vehicle owner database and, if a match is found, automatically generates and issues a challan (penalty). By automating the enforcement of traffic rules, the proposed system enhances road safety, reduces manual workload, and provides a scalable solution for intelligent traffic surveillance.},
        keywords = {Helmet detection, Motorcycle detection, YOLOv9, Easy OCR, Number plate recognition, Intelligent traffic system},
        month = {May},
        }