Applications of Computer Vision in Monitoring the Unsafe Behavior of Construction Workers: Current Status and Challenges

被引:47
作者
Liu, Wenyao [1 ]
Meng, Qingfeng [1 ]
Li, Zhen [1 ]
Hu, Xin [2 ]
机构
[1] Jiangsu Univ, Sch Management, 301 Xuefu Rd, Zhenjiang 212013, Jiangsu, Peoples R China
[2] Deakin Univ, Sch Architecture & Built Environm, 1 Gheringhap St, Geelong, Vic 3220, Australia
基金
中国国家自然科学基金;
关键词
computer vision; construction workers; monitoring; unsafe behavior; literature review; NEURAL-NETWORKS; AUTOMATED DETECTION; CLASSIFICATION; TRACKING; RECOGNITION; SYSTEM; FALLS; MODEL;
D O I
10.3390/buildings11090409
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
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.
引用
收藏
页数:27
相关论文
共 95 条
[1]   Deep learning in the construction industry: A review of present status and future innovations [J].
Akinosho, Taofeek D. ;
Oyedele, Lukumon O. ;
Bilal, Muhammad ;
Ajayi, Anuoluwapo O. ;
Delgado, Manuel Davila ;
Akinade, Olugbenga O. ;
Ahmed, Ashraf A. .
JOURNAL OF BUILDING ENGINEERING, 2020, 32
[2]  
[Anonymous], 2010, Commun. Surveys Tuts., DOI DOI 10.1038/nature14539
[3]  
[Anonymous], 2014, NONFATAL OCCUPATIONA
[4]  
[Anonymous], 1980, IND ACCIDENT PREVENT
[5]   Construction Activity Recognition and Ergonomic Risk Assessment Using a Wearable Insole Pressure System [J].
Antwi-Afari, Maxwell Fordjour ;
Li, Heng ;
Umer, Waleed ;
Yu, Yantao ;
Xing, Xuejiao .
JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT, 2020, 146 (07)
[6]   Automated detection and classification of construction workers' loss of balance events using wearable insole pressure sensors [J].
Antwi-Afari, Maxwell Fordjour ;
Li, Heng ;
Seo, JoonOh ;
Wong, Arnold Yu Lok .
AUTOMATION IN CONSTRUCTION, 2018, 96 :189-199
[7]   Wearable insole pressure system for automated detection and classification of awkward working postures in construction workers [J].
Antwi-Afari, Maxwell Fordjour ;
Li, Heng ;
Yu, Yantao ;
Kong, Liulin .
AUTOMATION IN CONSTRUCTION, 2018, 96 :433-441
[8]   A context-augmented deep learning approach for worker trajectory prediction on unstructured and dynamic construction sites [J].
Cai, Jiannan ;
Zhang, Yuxi ;
Yang, Liu ;
Cai, Hubo ;
Li, Shuai .
ADVANCED ENGINEERING INFORMATICS, 2020, 46
[9]  
Cavazza N, 2010, REV INT PSYCHOL SOC, V23, P187
[10]   Effects of safety climate on safety norm violations: exploring the mediating role of attitudinal ambivalence toward personal protective equipment [J].
Cavazza, Nicoletta ;
Serpe, Alessandra .
JOURNAL OF SAFETY RESEARCH, 2009, 40 (04) :277-283