Computer vision and IoT research landscape for health and safety management on construction sites

被引:13
|
作者
Arshad, Sameen [1 ]
Akinade, Olugbenga [2 ]
Bello, Sururah [1 ]
Bilal, Muhammad [1 ]
机构
[1] Univ West England UK, Big Data Enterprise & Artificial Intelligence Lab, Frenchay Campus, Bristol BS16 1QY, England
[2] Teesside Univ, Ctr Digital Innovat, Sch Comp Engn & Digital Technol, Dept Comp & Games, Middlesbrough TS1 3BX, England
来源
基金
“创新英国”项目;
关键词
BEHAVIOR-BASED SAFETY; NEURAL-NETWORKS; FRAMEWORK; WORK;
D O I
10.1016/j.jobe.2023.107049
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Aims: Perform a systematic review of current literature to evaluate and summarise the health and safety hazards on construction sites. Methods: Science Direct, SCOPUS and web of science databases were searched for research articles published from 2013 to 2021. From an initial search of 350 research articles, we removed the duplicate articles and carried out an analysis of the abstract and full text that focused on health, safety, hazards, behaviour, on-site health and safety and the digital technologies leaving a total of 66 studies included.Results: Computer vision and Internet of Things (IoT) are the dominant technologies for health and safety management. A comparison of the two technologies reveals that computer vision is dominant because of its non-intrusive approach to data collection; thus, supporting the scalability of computer vision approach at the expense of cost and development time. It will help to prevent on-site health and safety hazards and injuries on construction site. Conclusion: Computer vision offers non-intrusive benefits over Internet of Things (IoT); being able to detect the health and safety hazards. Com-puter vision has proved to be beneficial for better accuracy prediction, real time data monitoring, and model development for onsite health and safety analytics on the construction site.
引用
收藏
页数:12
相关论文
共 50 条
  • [1] Innovations in safety management for construction sites: the role of deep learning and computer vision techniques
    Mohy, Amr A.
    Bassioni, Hesham A.
    Elgendi, Elbadr O.
    Hassan, Tarek M.
    CONSTRUCTION INNOVATION-ENGLAND, 2024,
  • [2] Computer vision techniques for construction safety and health monitoring
    Seo, JoonOh
    Han, SangUk
    Lee, SangHyun
    Kim, Hyoungkwan
    ADVANCED ENGINEERING INFORMATICS, 2015, 29 (02) : 239 - 251
  • [3] Computer vision technologies for safety science and management in construction: A critical review and future research directions
    Guo, Brian H. W.
    Zou, Yang
    Fang, Yihai
    Goh, Yang Miang
    Zou, Patrick X. W.
    SAFETY SCIENCE, 2021, 135
  • [4] Computer vision-based safety risk computing and visualization on construction sites
    Hou, Xiaoyu
    Li, Chengqian
    Fang, Qi
    AUTOMATION IN CONSTRUCTION, 2023, 156
  • [5] IoT gateway and industrial safety with computer vision
    Zubal', M.
    Lojka, T.
    Zolotova, I.
    2016 IEEE 14TH INTERNATIONAL SYMPOSIUM ON APPLIED MACHINE INTELLIGENCE AND INFORMATICS (SAMI), 2016, : 183 - 186
  • [6] Construction Site Safety Management: A Computer Vision and Deep Learning Approach
    Lee, Jaekyu
    Lee, Sangyub
    SENSORS, 2023, 23 (02)
  • [7] LEGISLATIVE DEVELOPMENT AND MANAGEMENT HEALTH AND SAFETY IN MAINTENANCE CONSTRUCTION SITES
    Lagana, Renato
    SUSTAINABLE SOLUTIONS IN STRUCTURAL ENGINEERING AND CONSTRUCTION, 2014, : 665 - 670
  • [8] IMPERSONAL: An IoT-Aided Computer Vision Framework for Social Distancing for Health Safety
    Giuliano, Romeo
    Innocenti, Eros
    Mazzenga, Franco
    Vegni, Anna Maria
    Vizzarri, Alessandro
    IEEE INTERNET OF THINGS JOURNAL, 2022, 9 (10) : 7261 - 7272
  • [9] Combining computer vision with semantic reasoning for on-site safety management in construction
    Wu, Haitao
    Zhong, Botao
    Li, Heng
    Love, Peter
    Pan, Xing
    Zhao, Neng
    JOURNAL OF BUILDING ENGINEERING, 2021, 42
  • [10] Computer vision applications in construction safety assurance
    Fang, Weili
    Ding, Lieyun
    Love, Peter E. D.
    Luo, Hanbin
    Li, Heng
    Pena-Mora, Feniosky
    Zhong, Botao
    Zhou, Cheng
    AUTOMATION IN CONSTRUCTION, 2020, 110