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An enhanced YOLOv10 framework for small-object safety helmet detection on construction sites
Seunghyeon Wang1, En-Lian Zhang2, Rong-Lu Hong2
1Department of Cognitive Convergence Science, Hanyang University, Seongdong-gu, Seoul, 133791, Korea.
Scientific Reports
|July 8, 2026
Summary
This study enhances safety helmet compliance monitoring in surveillance images. The improved detector excels at identifying small safety helmets, even at long distances, boosting construction site safety.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Occupational Safety
Background:
- Monitoring safety helmet compliance in wide-area surveillance is difficult due to small helmet objects.
- Existing methods struggle with detecting helmets that occupy only a few pixels.
Purpose of the Study:
- To develop an enhanced object detection model for accurate safety helmet compliance monitoring.
- To address the challenge of small-object detection in surveillance imagery for safety applications.
Main Methods:
- An enhanced You Only Look Once version 10 (YOLOv10) detector was proposed.
- Key integrations include Omni-Dimensional Dynamic Convolution (ODConv), Efficient Multi-scale Attention-guided Bidirectional Feature Pyramid Network (EMA-BiFPN), a four-head detection scheme, and Minimum Points Distance Intersection over Union (MPDIoU) regression loss.
Main Results:
- The proposed detector achieved a mean Average Precision (mAP50) of 94.28%, outperforming the YOLOv10 baseline and other detectors.
- Significant performance gains were observed for extremely small and small helmet targets, with AP50 values of 86.10% and 91.55%, respectively.
- Improved localization of distant helmets was demonstrated.
Conclusions:
- The enhanced detector is a promising solution for deployment-focused safety helmet compliance monitoring in large-scale construction settings.
- Further improvements are needed for performance in far-field views and highly crowded scenes.
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