Intelligent Helmet Detection for Motorcyclists Using YOLO-Based Surveillance

Abstract

Motorcycle-related road accidents are a serious safety concern, particularly in areas where helmet use is not strictly observed. Helmets play a crucial role in reducing severe head injuries and fatalities, but manual enforcement is often inefficient and resource-heavy. This research introduces an automated system that detects helmet usage in real time by applying the YOLO deep learning framework along with convolutional neural networks. The model identifies motorcyclists and verifies helmet compliance with high accuracy, while being compatible with existing surveillance networks. Such systems reduce enforcement workload, encourage safer riding, and aim to minimize accident-related injuries

Introduction

Two-wheelers are widely used due to affordability and convenience, but helmet non-compliance remains a major cause of fatal injuries in accidents. Despite helmet laws, enforcement is often inconsistent and manual monitoring is resource-heavy and error-prone.

Traditional helmet enforcement relies on police checkpoints and basic computer vision methods like edge detection and color segmentation, which struggle in real-world conditions due to lighting and background variations. These manual and early automated systems lack scalability and reliability.

The proposed solution leverages the YOLO deep learning framework to create a real-time automated helmet detection system. It continuously monitors riders via surveillance cameras, quickly classifying helmet use with high accuracy. Violations are logged with images and metadata (date, time, location) to aid enforcement, reducing human effort and enhancing scalability.

The system’s methodology involves video capture, preprocessing (frame extraction and enhancement), YOLOv5-based detection, and violation logging. Evaluation results demonstrate strong performance, with high precision, recall, and balanced F1 scores, confirming the system’s effectiveness and reliability in practical scenarios.

Conclusion

This study validates the use of a YOLO-based system for automated helmet detection. The framework integrates effectively with existing traffic surveillance, enabling accurate, real-time monitoring without requiring excessive manual effort. By leveraging transfer learning and optimized datasets, the system achieved strong detection results with relatively small amounts of training data. Such solutions have the potential to support authorities in enhancing road safety and reducing accident-related fatalities.

References

[1] Kumar, R., & Singh, A. (2023). Deep learning methods for helmet detection in urban traffic. Journal of Intelligent Transportation Systems, 18(2), 145–158. [2] Verma, P., & Yadav, S. (2022). YOLO-based real-time object detection for traffic safety. International Journal of Computer Vision Research, 14(3), 201–214. [3] Deshmukh, M., & Patil, R. (2021). License plate recognition with helmet compliance monitoring. IEEE Conference on Smart City Technologies, 56–62. [4] Alvi, M., & Rahman, T. (2022). Comparative analysis of YOLOv5 and YOLOv8 for traffic surveillance. Proceedings of the International Conference on AI in Transportation, 89–97. [5] Choudhary, D., & Sharma, K. (2023). Low-light image enhancement for helmet detection. Journal of Computer Vision and Image Processing, 11(1), 77–85.

Copyright

Copyright © 2025 Mr. Shaik Mehaboob Basha, Prof. Mr. Prakash Sarangamath. This 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.