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YOLO-based intelligent recognition system for hidden dangers at construction sites

Author

Listed:
  • Hang Li
  • Peijian Jin
  • Long Zhan
  • Weilong Yan
  • Shihao Guo
  • Shimei Sun

Abstract

To reduce the accident rate in the construction industry, an improved YOLOv5n-based hazard recognition system for construction sites is proposed. By incorporating optimization mechanisms such as the ECA attention module, ghost module, SIoU loss, and EIoU–NMS into YOLOv5n, the system achieves both lightweight acceleration and improved accuracy. Two ultrasmall models (approximately 2.5 MBs each) were trained on a self-built dataset to detect “unsafe human behaviors” and “unsafe object conditions,” achieving mAP@0.5 scores of 93.6% and 99.5%, respectively. After deployment on the Jetson Nano B01 edge platform, the system was constructed, and its high efficiency in onsite hazard detection was validated.

Suggested Citation

  • Hang Li & Peijian Jin & Long Zhan & Weilong Yan & Shihao Guo & Shimei Sun, 2025. "YOLO-based intelligent recognition system for hidden dangers at construction sites," PLOS ONE, Public Library of Science, vol. 20(9), pages 1-30, September.
    • Handle: RePEc:plo:pone00:0332042
    DOI: 10.1371/journal.pone.0332042
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