Proceedings of the International Conference on Advances and Applications in Artificial Intelligence (ICAAAI 2025)

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Automated Helmet Detection Using YOLOv5 for Enhanced Road Safety Compliance

Authors
A. Pavan Kumar1, *, A. Gopi Chand1, H. Thanu Shree1, S. Sreenivas1, V. Bharath1, Tina Babu1
1Department of Computer Science and Engineering, Alliance School of Advanced Computing, Alliance Univerity, Bengaluru, India
*Corresponding author. Email: panimelabtech22@ced.alliance.edu.in
Corresponding Author
A. Pavan Kumar
Available Online 22 June 2025.
DOI
10.2991/978-94-6463-738-0_10How to use a DOI?
Keywords
Helmet Detection; YOLOv5; Computer Vision; Traffic Safety; Real-Time Object Detection; Road Compliance; Motorcyclist Safety
Abstract

Here’s a clearer and more concise version of the abstract: This study presents a robust helmet detection system using computer vision techniques and the YOLOv5 model. Using Attributed to Table (VTT), we aim to identify motorcycle riders who violate safety regulations by not wearing helmets, thereby endangering their lives. With the increasing prevalence of two-wheeled vehicles, automatic helmet detection can help reduce the rising number of fatal and severe road accidents. We trained the model on a custom dataset derived from accident analysis, implementing various data preprocessing and augmentation techniques to enhance prediction accuracy and adaptability across different riding conditions. YOLOv5 was chosen for its real-time detection capabilities in both images and videos. The model’s performance was evaluated using metrics, including accuracy, recall, and F1-score. This research demonstrates how such a system could be integrated into existing traffic monitoring infrastructure to enable automated enforcement of safety regulations.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Advances and Applications in Artificial Intelligence (ICAAAI 2025)
Series
Advances in Intelligent Systems Research
Publication Date
22 June 2025
ISBN
978-94-6463-738-0
ISSN
1951-6851
DOI
10.2991/978-94-6463-738-0_10How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - A. Pavan Kumar
AU  - A. Gopi Chand
AU  - H. Thanu Shree
AU  - S. Sreenivas
AU  - V. Bharath
AU  - Tina Babu
PY  - 2025
DA  - 2025/06/22
TI  - Automated Helmet Detection Using YOLOv5 for Enhanced Road Safety Compliance
BT  - Proceedings of the International Conference on Advances and Applications in Artificial Intelligence (ICAAAI 2025)
PB  - Atlantis Press
SP  - 116
EP  - 126
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-738-0_10
DO  - 10.2991/978-94-6463-738-0_10
ID  - Kumar2025
ER  -