Citation

Chian EYT, Goh YM, Tian J, Guo BHW. Safety Sci. 2022; 156: e105904.

Copyright

(Copyright © 2022, Elsevier Publishing)

DOI

10.1016/j.ssci.2022.105904

PMID

unavailable

Abstract

Crane activities pose significant safety risks on construction sites. Thus, numerous studies have applied computer vision techniques to identify crane load fall zones or areas where the crane load could fall during an accident. However, past studies typically assumed that the fall zone directly below the load was static and had a fixed size. In reality, the fall zone is dynamic because its size changes with different crane load types and heights. Thus, this study proposes and validates a vision-based method that dynamically identifies the fall zone directly below the load. The proposed dynamic fall zone detection and tracking method first detects the key points of the load, and then the fall zone is estimated by projecting the area of the lifted load to the construction floor. Once the fall zone is detected and tracked, coupled with a previously established approach for worker detection and tracking, workers entering the fall zone can be automatically detected. Data were collected from a public housing project in Singapore to train and evaluate the proposed method. The proposed method achieved precision and recall of 0.86 and 0.83, respectively. The main contribution of this study is the development of a novel method to detect and track the crane load fall zone. Besides providing real-time safety alerts to crane operators and site supervisors, the proposed method can accumulate statistics on unsafe behaviour and situations to facilitate safety management.

Language: en

Keywords

Behaviour-based safety; Computer vision; Crane safety; Deep learning; Lifting operation