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YOLO-based intelligent recognition system for hidden dangers at construction sites
Hang Li1, Peijian Jin1,2, Long Zhan3
1School of Emergency Science and Engineering, Jilin Jianzhu University, Changchun, Jilin, China.
Plos One
|September 17, 2025
Summary
This study introduces an improved YOLOv5n hazard recognition system for construction sites, enhancing safety through lightweight design and high accuracy in detecting unsafe behaviors and conditions.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Construction Safety
Background:
- Construction industry faces high accident rates.
- Existing hazard recognition systems may lack efficiency or accuracy.
Purpose of the Study:
- To develop an optimized, lightweight hazard recognition system for construction sites.
- To improve the detection of unsafe human behaviors and unsafe object conditions.
Main Methods:
- Incorporated ECA attention, ghost module, SIoU loss, and EIoU-NMS into YOLOv5n.
- Trained two ultra-small models (approx. 2.5 MBs) on a custom dataset.
- Deployed the system on a Jetson Nano B01 edge platform.
Main Results:
- Achieved high mean Average Precision (mAP@0.5) scores: 93.6% for unsafe human behaviors and 99.5% for unsafe object conditions.
- Demonstrated lightweight acceleration and improved accuracy.
- Validated efficient onsite hazard detection capabilities.
Conclusions:
- The proposed YOLOv5n-based system effectively enhances construction site safety.
- The optimized model offers a practical solution for real-time, edge-based hazard detection.
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