Citation

Hu Q, Bai Y, He L, Huang J, Wang H, Cheng G. Sustainability (Basel) 2022; 14(10): e6126.

Copyright

(Copyright © 2022, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/su14106126

PMID

unavailable

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.

Language: en

Keywords

construction site; deep learning; intelligent recognition; unsafe actions; working at heights