Applications of Computer Vision in Monitoring the Unsafe Behavior of Construction Workers: Current Status and Challenges
Author(s): |
Wenyao Liu
Qingfeng Meng Zhen Li Xin Hu |
---|---|
Medium: | journal article |
Language(s): | English |
Published in: | Buildings, 25 August 2021, n. 9, v. 11 |
Page(s): | 409 |
DOI: | 10.3390/buildings11090409 |
Abstract: |
The unsafe behavior of construction workers is one of the main causes of safety accidents at construction sites. To reduce the incidence of construction accidents and improve the safety performance of construction projects, there is a need to identify risky factors by monitoring the behavior of construction workers. Computer vision (CV) technology, which is a powerful and automated tool used for extracting images and video information from construction sites, has been recognized and adopted as an effective construction site monitoring technology for the identification of risky factors resulting from the unsafe behavior of construction workers. In this article, we introduce the research background of this field and conduct a systematic statistical analysis of the relevant literature in this field through the bibliometric analysis method. Thereafter, we adopt a content-based analysis method to depict the historical explorations in the field. On this basis, the limitations and challenges in this field are identified, and future research directions are proposed. It is found that CV technology can effectively monitor the unsafe behaviors of construction workers. The research findings can enhance people’s understanding of construction safety management. |
Copyright: | © 2021 by the authors; licensee MDPI, Basel, Switzerland. |
License: | This creative work has been published under the Creative Commons Attribution 4.0 International (CC-BY 4.0) license which allows copying, and redistribution as well as adaptation of the original work provided appropriate credit is given to the original author and the conditions of the license are met. |
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data sheet - Reference-ID
10631242 - Published on:
01/10/2021 - Last updated on:
05/10/2021