A deep learning-based method for detecting non-certified work on construction sites |
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| Affiliation: | 1. Dept. of Construction Management, School of Civil Engineering and Mechanics, Huazhong University of Science and Technology, Wuhan, Hubei, China;2. Hubei Engineering Research Center for Virtual, Safe and Automated Construction, China;3. Dept. of Civil Engineering, Curtin University, Perth, Western Australia, Australia;4. School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan, Hubei, China;1. Department of Civil and Environmental Engineering, Myongji University, 4th Engineering Building, 116 Myongji-ro, Yongin, Gyeonggi-do 449-728, South Korea;2. Department of Engineering, University of Cambridge, BC2-07, Trumpington Street, Cambridge CB2 1PZ, UK;1. Department of Building and Real Estate, Faculty of Construction and Environment, Hong Kong Polytechnic University, Room No. ZN1002, Hung Hom, Kowloon, Hong Kong Special Administrative Region;2. Department of Building and Real Estate, Faculty of Construction and Environment, Hong Kong Polytechnic University, Room No. ZS734, Hung Hom, Kowloon, Hong Kong Special Administrative Region;1. Department of Building and Real Estate, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China;2. Department of Civil Engineering and Mechanics, Huazhong University of Science and Technology, Wuhan, China;3. Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China;1. Dept. of Building and Real Estate, Hong Kong Polytechnic Univ., Hung Hom, Kowloon, Hong Kong;2. Dept. of Construction Management and Real Estate, School of Economics and Management, Tongji Univ., 1239 Siping Rd., Shanghai 200092, China |
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| Abstract: | The construction industry is a high hazard industry. Accidents frequently occur, and part of them are closely relate to workers who are not certified to carry out specific work. Although workers without a trade certificate are restricted entry to construction sites, few ad-hoc approaches have been commonly employed to check if a worker is carrying out the work for which they are certificated. This paper proposes a novel framework to check whether a site worker is working within the constraints of their certification. Our framework comprises key video clips extraction, trade recognition and worker competency evaluation. Trade recognition is a new proposed method through analyzing the dynamic spatiotemporal relevance between workers and non-worker objects. We also improved the identification results by analyzing, comparing, and matching multiple face images of each worker obtained from videos. The experimental results demonstrate the reliability and accuracy of our deep learning-based method to detect workers who are carrying out work for which they are not certified to facilitate safety inspection and supervision. |
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| Keywords: | Construction safety Certification checking Trade recognition Identification Deep learning |
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