Advertisement

A Real-Time Automated Approach for Ensuring Proper Use of Personal Protective Equipment (PPE) in Construction Site

Conference paper
  • 1.7k Downloads
Part of the Lecture Notes in Civil Engineering book series (LNCE, volume 98)

Abstract

Construction sites are one of the most perilous environments where many potential hazards may occur. Even though workers are trained to stay away from potential dangers, there are still many types of risks that can occur within only a few minutes of carelessness. Personal Protective Equipment (PPE) is an important safety measure used to protect construction workers from accidents. However, PPE usage is not strictly enforced among workers due to all kinds of reasons. This paper proposes the combination of deep learning-based object detection and individual detection using geometry relationships analysis to automatically identify non-PPE-use (NPU); i.e., if a worker is wearing hardhat, eye protection visors, dust masks, or both, to help to facilitate the safety monitoring work of construction workers to ensure PPE are appropriately used. The experimental results demonstrate that the approach was capable of detecting NPU workers with high precision (84.13%) and recall rate (93.10%) while ensuring real-time performance (7.95 FPS on average).

Keywords

Construction safety Personal Protective Equipment (PPE) Deep learning Object detection 

Notes

Acknowledgements

This work was supported by Japan Society for the Promotion of Science (JSPS) Grants-in-Aid for Scientific Research Grant No. 19K05324.

References

  1. 1.
    U.S. Bureau of Labor Statistics, Construction: NAICS 23. https://www.bls.gov/iag/tgs/iag23.htm. Accessed 13 Jan 2020
  2. 2.
    The Japanese Ministry of Health, Welfare, The 13th occupational safety health program. https://www.mhlw.go.jp/content/11200000/000341159.pdf. Accessed 13 Jan 2020
  3. 3.
    Konda, S., Tiesman, H.M., Reichard, A.A.: Fatal traumatic brain injuries in the construction industry, 2003–2010. Am. J. Ind. Med. 59(3), 212–220 (2016)CrossRefGoogle Scholar
  4. 4.
    U.S. Department of Labor, Occupational Safety and Health Administration, Head protection. https://www.osha.gov/laws-regs/regulations/standardnumber/1926/1926.100. Accessed 26 Jan 2020
  5. 5.
    The National Institute for Occupational Safety and Health, Eye safety. https://www.cdc.gov/niosh/topics/eye/. Accessed 13 Jan 2020
  6. 6.
    Dannenberg, A.L., Parver, L.M., Brechner, R.J., Khoo, L.: Penetrating eye injuries in the workplace: the national eye trauma system registry. Arch. Ophthalmol. 110(6), 843–848 (1992)CrossRefGoogle Scholar
  7. 7.
    Government of Western Australia, Department of Commerce, Guide to using dust masks in construction work (2019). https://www.commerce.wa.gov.au/sites/default/files/atoms/files/guide_to_using_dust_mask.pdf
  8. 8.
    U.S. Department of Labor, Occupational Safety and Health Administration, Eye and face protection. https://www.osha.gov/laws-regs/regulations/standardnumber/1910/1910.133. Accessed 13 Jan 2020
  9. 9.
    Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement, arXiv preprint arXiv:1804.02767
  10. 10.
    Cao, Z., Hidalgo, G., Simon, T., Wei, S.E., Sheikh, Y.: Openpose: realtime multi-person 2D pose estimation using part affinity fields, arXiv preprint arXiv:1812.08008
  11. 11.
    Kelm, A., Laußat, L., Meins-Becker, A., Platz, D., Khazaee, M.J., Costin, A.M., Helmus, M., Teizer, J.: Mobile passive Radio Frequency Identification (RFID) portal for automated and rapid control of Personal Protective Equipment (PPE) on construction sites. Autom. Construct. 36, 38–52 (2013)CrossRefGoogle Scholar
  12. 12.
    Dong, S., He, Q., Li, H., Yin, Q.: Automated PPE misuse identification and assessment for safety performance enhancement. In ICCREM 2015, pp. 204–214 (2015)Google Scholar
  13. 13.
    Shrestha, K., Shrestha, P.P., Bajracharya, D., Yfantis, E.A.: Hard-hat detection for construction safety visualization. Journal of Construction Engineering 2015, 1–8 (2015)CrossRefGoogle Scholar
  14. 14.
    Park, M.W., Elsafty, N., Zhu, Z.: Hardhat-wearing detection for enhancing on-site safety of construction workers. J. Construct. Eng. Manage. 141(9), 04015024 (2015)CrossRefGoogle Scholar
  15. 15.
    Fang, Q., Li, H., Luo, X., Ding, L., Luo, H., Rose, T.M., An, W.: Detecting non-hardhat-use by a deep learning method from far-field surveillance videos. Autom. Construction 85, 1–9 (2018)CrossRefGoogle Scholar
  16. 16.
    Wu, J., Cai, N., Chen, W., Wang, H., Wang, G.: Automatic detection of hardhats worn by construction personnel: a deep learning approach and benchmark dataset. Autom. Construction 106, 102894 (2019)CrossRefGoogle Scholar
  17. 17.
    Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: common objects in context. In: European Conference on Computer Vision, pp. 740–755. Springer, Cham (2014)Google Scholar
  18. 18.
    Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetV2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)Google Scholar
  19. 19.
    Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv preprint arXiv:1409.1556
  20. 20.
    Yang, S., Luo, P., Loy, C.C., Tang, X.: Wider face: a face detection benchmark. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5525–5533 (2016)Google Scholar
  21. 21.
    Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)MathSciNetCrossRefGoogle Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021

Authors and Affiliations

  1. 1.The University of TokyoTokyoJapan
  2. 2.IIU CorporationTokyoJapan

Personalised recommendations