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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

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).

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