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Methodology for Near-miss Identification between Earthwork Equipment and Workers using Image Analysis

영상분석기법을 활용한 토공 장비 및 작업자간 아차사고식별 방법론

  • Lim, Tae-Kyung (Intelligent Construction Automation Center, Kyungpook National University) ;
  • Choi, Byoung-Yoon (School of Architecture, Environmental, Energy and Civil Engineering, Kyungpook National University) ;
  • Lee, Dong-Eun (Sch. of Arch & Civil Engrg, Kyungpook National Uviversity)
  • 임태경 (경북대학교 지능형건설자동화 연구센터) ;
  • 최병윤 (경북대학교 건설환경에너지공학부) ;
  • 이동은
  • Received : 2019.01.19
  • Accepted : 2019.02.07
  • Published : 2019.07.31

Abstract

This paper presents a method that identifies the unsafe behaviors at the level of near-misses using image analysis. The method establishes potential collision hazardous area in earthmoving operation. It is implemented using a game engine to reproduce the dangerous events that have been accepted as major difficulty in utilizing computer vision technology to support construction safety management. The method keeps realistically track of the ever-changing hazardous area by reflecting the volatile field conditions. The method opens a way to distinguish unsafe conditions and unsafe behaviors that have been overlooked in previous studies, and reflects the causal relationship which causes an accident. The case study demonstrate how to identify the unsafe behavior of a worker exposed to an unsafe area created by dump trucks at the level of near-misses and to determine the hazardous areas.

본 연구는 토사운반작업이 실행되는 현장에 충돌위험구역을 설정하고 작업자의 불안전한 행동을 아차사고수준에서 식별하는 영상분석 방법론을 제시한다. 컴퓨터 비전기술을 건설안전관리에 활용하는 데 있어 큰 걸림돌이 되어 온 위험발생 이벤트를 연구자가 원하는 시나리오대로 재현하기 용이하도록 게임엔진을 활용하는 방법을 제시한다. 본 연구는 기존 연구들이 불안전한 조건을 결정론적으로 가정하는 접근방식과 달리, 현장여건에 따라 위험구역이 변화되는 상황을 현실적으로 반영하는 방법을 제시한다. 본 방법론은 선행연구들이 간과한 불안전한 조건과 행동을 구분하는 방법을 제시하고 사고가 발생되는 인과관계를 반영하였다. 사례연구는 덤프트럭에 의해 제공된 불안전한 조건하에서 작업자의 불안전한 행동을 아차사고 수준에서 관측하는 방법과 중점관리 대상이 되는 위험구역을 결정하는 방법을 규명하였다.

Keywords

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Fig. 1. Construction Site Survey

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Fig. 2. A virtual model of earthmoving work using UGE

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Fig. 3. The result of difference image and blob analysis

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Fig. 4. Truck identification

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Fig. 5. Truck route and hazardous boundary

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Fig. 6. Identification of worker's near-miss

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