Journal of the Korea Institute of Building Construction (한국건축시공학회지)

The Korean Institute of Building Construction (한국건축시공학회)
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Accuracy Analysis of Construction Worker's Protective Equipment Detection Using Computer Vision Technology

컴퓨터 비전 기술을 이용한 건설 작업자 보호구 검출 정확도 분석

  • Kang, Sungwon (Department of Architectural Engineering, Kyonggi University) ;
  • Lee, Kiseok (Department of Architectural Engineering, Kyonggi University) ;
  • Yoo, Wi Sung (Department of Economic and Financial Research, Construction & Economy Research Institute of Korea) ;
  • Shin, Yoonseok (Department of Architectural Engineering, Kyonggi University) ;
  • Lee, Myungdo (R&D Center, Yunwoo Technologies Co., Ltd.)
  • Received : 2022.12.01
  • Accepted : 2023.01.09
  • Published : 2023.02.20
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Abstract

According to the 2020 industrial accident reports of the Ministry of Employment and Labor, the number of fatal accidents in the construction industry over the past 5 years has been higher than in other industries. Of these more than 50% of fatal accidents are initially caused by fall accidents. The central government is intensively managing falling/jamming protection device and the use of personal protective equipment to eradicate the inappropriate factors disrupting safety at construction sites. In addition, although efforts have been made to prevent safety accidents with the proposal of the Special Act on Construction Safety, fatalities on construction sites are constantly occurring. Therefore, this study developed a model that automatically detects the wearing state of the worker's safety helmet and belt using computer vision technology. In considerations of conditions occurring at construction sites, we suggest an optimization method, which has been verified in terms of the accuracy and operation speed of the proposed model. As a result, it is possible to improve the efficiency of inspection and patrol by construction site managers, which is expected to contribute to reinforcing competency of safety management.

Keywords

Acknowledgement

This study was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government (Ministry of Education) in 2022(2021R1A2C2013841).

References

  1. Ministry of Employment and Labor. Status of industrial accidents in 2021 [Internet]. Sejong (Korea): Ministry of Employment and Labor; 2022 Mar 16. Available From: https://www.moel.go.kr/skin/doc.html?fn=20220316181319d8e53bb1623f43478809a5e10076b441.hwp&rs=/viewer/BBS/2022/
  2. Ministry of Employment and Labor. Statistics on the deaths of industrial accidents in 2020 [Internet]. Sejong (Korea): Ministry of Employment and Labor; 2021 Dec 30. Available From: https://www.moel.go.kr/skin/doc.html?fn=2022090115531410acc82f7e9946809d3b368b4b88a393.hwpx&rs=/viewer/BBS/2022/
  3. Korea Occupational Safety Health Agency (KOSHA). Safety and Health Plus in May [Internet]. Ulsan (Korea): Korea Occupational Safety Health Agency (KOSHA); 2021 Apr 27. Available From: https://www.kosha.or.kr/ebook/fcatalog/ecatalog5.jsp?Dir=493&catimage=&listCall=Y&eclang=ko
  4. Korea Law Information Center. Enforcement Rule Of The Construction Technology Promotion Act. Sejong (Korea): Korea Law Information Center; 2022 Dec 30. Available From: https://www.law.go.kr/LSW/lsSc.do?section=&menuId=1&sub MenuId=15&tabMenuId=81&eventGubun=060101&query=%EA%B1%B4%EC%84%A4%EA%B8%B0%EC%88%A0%EC%A7%84%ED%9D%A5%EB%B2%95+%EC%8B%9C%ED%96%89%EA%B7%9C%EC%B9%99#undefined
  5. Ministry of Land, Infrastructure and Transport. Guidelines for Smart Construction Technology Field Application [Internet]. Sejong (Korea): Ministry of Land, Infrastructure and Transport; 2021 Mar 4. Available From: http://m.molit.go.kr/viewer/skin/doc.html?fn=57efd7f626b212c57f4959468f9208ef&rs=/viewer/result/20210304
  6. Ministry of Land, Infrastructure and Transport. Improving the Safety of Construction Sites by Introducing Smart Safety Equipment [Internet]. Sejong (Korea): Ministry of Land, Infrastructure and Transport; 2020 Mar 23. Available From: http://m.molit.go.kr/viewer/skin/doc.html?fn=ba4f21a0718d1688945470e7c83f2168&rs=/viewer/result/20200320
  7. Ministry of Land, Infrastructure and Transport. Smart Construction Technology Roadmap [Internet]. Sejong (Korea): Ministry of Land, Infrastructure and Transport; 2018 Oct 31. Available From: http://m.molit.go.kr/viewer/skin/doc.html?fn=48b98c06c65155bf1c8c1a5289011bf2&rs=/viewer/result/20181031
  8. Lee KP, Choi SY, Son TH, Choi SI. Survey on smart technology applications of korean construction companies and strategies for activation. Seoul (Korea): Construction & Economy Research Institute of Korea Research; 2019 Dec 26. Available From: http://www.cerik.re.kr/report/research/detail/2330
  9. Lee CH, Kim IS, Lee CY, Shin JM, Kang CH. Application of smart construction technology for quality and productivity improvement. Magazine of the Korea Concrete Institue. 2021 Nov;33(6):67-72.
  10. Kim WB. Apreliminary study on computer vision based safety helmet detection in constrction [master's thrsis]. [Busan (Korea)]: Pukyong National University; 2018. 52 p.
  11. Kim MH. Application of deep learning technique for detecting constructio worker wearing safety helmet based on computer vision [master's thrsis]. [Busan (Korea)]: Pukyong National University; 2019. 69 p.
  12. Shin JK. Real-time monitoring system for personal protective equipment of construction worker using smart technology [Ph.D. dissertation]. [Suwon (Korea)]: Kyonggi University. 2022. 188 p.
  13. Lee DH. A study on object detection and sensor fusion based on deep learning using lidar and camer [master's thrsis]. [Seoul (Korea)]: Kookmin University; 2020. 83 p.
  14. Lee YH, Kin YS. Comparison of cnn and yolo for object detection. Journal of the Semiconductor & Display Technology. 2020 Mar;19(1):85-90.
  15. Park YS, Lee SY, Lee KT, Improving personal protective equipment detectionin smart construction. The Journal of the KICS. 2020 Nov;45(12):2202-9. https://doi.org/10.7840/kics.2020.45.12.2202
  16. Jeon SY, Park JH, Youn SB, Kim YS, Lee YS, Jeon JH. Real-time worker safety management system using deep learning-based video analysis algorithm. Smart Media Journal. 2020 Sep;9(3):25-30. https://dx.doi.org/10.30693/SMJ.2020.9.3.25
  17. Nipun DN, Behzaden AH, Paal SG. Deep learning for site safety: Real-time detection of personal protective equipment. Automation in Construction. 2020 Apr;112:103085. https://doi.org/10.1016/j.autcon.2020.103085
  18. Jeong IS, Kim JW, Chi SH, Roh MG, Biggs H. Solitary work detection of heavy equipment using computer vision. KSCE Journal of Civil and Environmental Engineering Research. 2021 Aug;41(4):441-7. https://doi.org/10.12652/Ksce.2021.41.4.0441
  19. Jo BW, Lee YS, Kim DK, Kim JH, Choi PH. Image-based proximity warning system for excavator of construction sites. The Journal of the Korea Contents Association. 2016 Aug;16(10):588-97. https://doi.org/10.5392/JKCA.2016.16.10.588
  20. Cho YW, Kang KS, Son BS, Ryu HG. Extraction of workers and heavy equipment and muliti-object tracking using surveillance system in construction sites. Journal of The Korea Institute of Building Construction. 2021 Oct;21(5):397-408. https://doi.org/10.5345/JKIBC.2021.21.5.397
  21. Lim TK, Choi BY, Lee DE. Methodology for near-miss identification between earthwork equipment and workers using image analysis. Korean Journal of Construction Engineering and Management. 2019 Jul;20(4):69-76. https://doi.org/10.6106/KJCEM.2019.20.4.069
  22. Jeon CW. Deep-learning based detection for small to medium sized tools in indoor construction site [master's thrsis]. [Incheon (Korea)]: Inha University. 2021. 70 p.
  23. Shin DH, A study on Railway Safety Enhancement on the Utilizing Open Source Computer Vision [Ph.D. dissertation]. [Chungju (Korea)]: Korea National University of Transportation. 2019. 134 p.
  24. Park GJ, Binayark B. Railway facility real-time intruder monitoring system using computer vision and deep learning. Journal of the Korean Society for Railway. 2020 Jan;23(1):35-44. https://doi.org/10.7782/JKSR.2020.23.1.35