IDEAS home Printed from https://ideas.repec.org/a/bjf/journl/v10y2025i4p902-910.html
   My bibliography  Save this article

A Deep Learning Approach for Helmet Detection and Fine Generation System

Author

Listed:
  • Sulbha Yadav

    (Department of Computer Engineering, Lokmanya Tilak College of Engineering, Navi Mumbai, India)

  • Sumit Singh

    (Department of Computer Engineering, Lokmanya Tilak College of Engineering, Navi Mumbai, India)

  • Dhruv Bedare

    (Department of Computer Engineering, Lokmanya Tilak College of Engineering, Navi Mumbai, India)

  • Ishan Samel

    (Department of Computer Engineering, Lokmanya Tilak College of Engineering, Navi Mumbai, India)

Abstract

Traffic law violations, especially helmet non-compliance among motorcyclists, are a leading cause of road casualties in countries like India. This study proposes a real-time deep learning-based helmet detection and fine generation system that leverages YOLOv3 for object detection and OpenCV with OCR for license plate recognition. The system processes surveillance footage to detect violations and automatically generates e-challans by integrating with vehicle databases. Experimental results show an accuracy of over 95% for helmet detection. This automated solution reduces manual workload, supports road safety enforcement, and has the potential for integration into smart city infrastructure. Future work may involve multilingual plate recognition and improved database interoperability.

Suggested Citation

  • Sulbha Yadav & Sumit Singh & Dhruv Bedare & Ishan Samel, 2025. "A Deep Learning Approach for Helmet Detection and Fine Generation System," International Journal of Research and Innovation in Applied Science, International Journal of Research and Innovation in Applied Science (IJRIAS), vol. 10(4), pages 902-910, April.
    • Handle: RePEc:bjf:journl:v:10:y:2025:i:4:p:902-910
    as

    Download full text from publisher

    File URL: https://www.rsisinternational.org/journals/ijrias/digital-library/volume-10-issue-4/902-910.pdf
    Download Restriction: no
    File URL: https://rsisinternational.org/journals/ijrias/articles/a-deep-learning-approach-for-helmet-detection-and-fine-generation-system/
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bjf:journl:v:10:y:2025:i:4:p:902-910. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Dr. Renu Malsaria (email available below). General contact details of provider: https://rsisinternational.org/journals/ijrias/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.