A deep learning-based method for detecting non-certified work on construction sites. (January 2018) - Fang, Qi; Li, Heng; Luo, Xiaochun; Ding, Lieyun; Rose, Timothy M.; An, Wangpeng; Yu, Yantao
Highlights: We proposed a framework to detect non-certified work for the first time. Spatiotemporal relevance is analyzed for trade recognition. An improved identification strategy is proposed. Experimental results demonstrate our solution is effective and feasible. 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.