Real-Time Helmet Compliance Detection for Motorcycle Riders Using YOLO

Deployment on Low-Cost Traffic Cameras

Rizky Pratama, Nadia Safitri

Jurnal Teknologi dan Inovasi, Vol. 1 No. 2 (2026), pp. 41-48

Abstract

Helmet non-compliance remains a major factor in motorcycle fatalities, yet manual enforcement covers only a fraction of traffic. This paper presents a helmet compliance detector based on the YOLO family of single-stage object detectors, trained on 14,200 annotated frames from intersection cameras with varied lighting and weather. The best model reached a mean average precision of 0.89 at an IoU threshold of 0.5 and ran at 27 frames per second on an embedded GPU module. Most errors involved riders partly hidden by other vehicles or wearing caps that resemble helmets. A four-week pilot at two intersections produced aggregate compliance statistics that local authorities used to plan targeted safety campaigns. The system is designed to report anonymous counts rather than identify individual riders.

Keywords: object detection; YOLO; traffic safety; helmet detection; deep learning

1. Introduction

This section introduces the problem of computer vision addressed in this study, reviews the main approaches reported in prior work, and states the research objectives.

2. Methods

We describe the data, the proposed approach, the baselines, and the evaluation protocol used to assess the results summarised in the abstract.

3. Results and Discussion

The results are presented and compared with the baselines. Limitations and practical implications for deployment are discussed.

4. Conclusion

The study shows that the proposed approach is effective for the stated objectives and outlines directions for future work.

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