Workers’ Unsafe Actions When Working at Heights: Detecting from Images

Qijun Hu, Yu Bai, Leping He, Jie Huang, Haoyu Wang and Guangran Cheng
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Qijun Hu: School of Mechatronic Engineering, Southwest Petroleum University, Chengdu 610500, China
Yu Bai: School of Mechatronic Engineering, Southwest Petroleum University, Chengdu 610500, China
Leping He: School of Civil Engineering and Geomatics, Southwest Petroleum University, Chengdu 610500, China
Jie Huang: CECEP Construction Engineering Design Institute Limited, Chengdu 610052, China
Haoyu Wang: College of Physics, Chongqing University, Chongqing 400044, China
Guangran Cheng: CECEP Construction Engineering Design Institute Limited, Chengdu 610052, China

Sustainability, 2022, vol. 14, issue 10, 1-15

Abstract: Working at heights causes heavy casualties among workers during construction activities. Workers’ unsafe action detection could play a vital role in strengthening the supervision of workers to avoid them falling from heights. Existing methods for managing workers’ unsafe actions commonly rely on managers’ observation, which consumes a lot of human resources and impossibly covers a whole construction site. In this research, we propose an automatic identification method for detecting workers’ unsafe actions, considering a heights working environment, based on an improved Faster Regions with CNN features (Faster R-CNN) algorithm. We designed and carried out a series of experiments involving five types of unsafe actions to examine their efficiency and accuracy. The results illustrate and verify the method’s feasibility for improving safety inspection and supervision, as well as its limitations.

Keywords: unsafe actions; working at heights; intelligent recognition; deep learning; construction site (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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