Research Article
Open access
Published on 24 January 2024
Download pdf
Wang,Y. (2024). Research on computer vision application for safety management in construction. Theoretical and Natural Science,30,232-242.
Export citation

Research on computer vision application for safety management in construction

Yu Wang *,1,
  • 1 Huazhong University of Science & Technology

* Author to whom correspondence should be addressed.

https://doi.org/10.54254/2753-8818/30/20241124

Abstract

In light of the complexity of building sites, safety management has always been a challenging and crucial duty. The goal of construction safety management is to ensure the safe completion of the project, maintain the life and health of workers, and decrease construction accidents and losses. The emergence of computer vision (CV) technology has transformed the old construction safety management methodology. This paper demonstrates how CV technology can increase safety and management efficiency at construction sites from an application standpoint. Firstly, the principles and methods of CV technology are introduced, and the literature analysis methodology used in this paper is also descripted in detail. Then, its application in various aspects such as real-time monitoring of construction sites, worker safety management, equipment behaviour monitoring and material quality control are researched. In addition, this paper demonstrates the significant potential of CV technology in reducing accidents and enhancing safety performance, and provides important insights into future research prospects. Finally, this paper presents a more detailed picture of the construction industry’s aim to utilize CV technology to improve safety management, and is both informative and instructional.

Keywords

Computer vision, safety management, worker behavior

[1]. Ministry of Housing and Urban-Rural Development of the People’s Republic of China General Office of the Ministry of Housing and Urban-Rural Development on Production Safety Accidents in Housing and Municipal Engineering in 2020. 21 May 2021, Retrieved on November 13, 2023, Retrieved from: https://www.mohurd.gov.cn/ gongkai/zhengce/zhengcefilelib/202210/20221026_768565.html

[2]. International Labour Office 2005 Facts about safety at work iRetrieved on November 13, 2023, Retrieved from: https://www.ilo.org/wcmsp5/groups/public/---asia/---ro-bangkok/---ilo-beijing/documents/publication/wcms_142901.pdf

[3]. Wang M, Wong P, Luo H, Kumar S, Delhi V and Cheng J 2019 Proc. Inter. Symposium on Automation and Robotics in Construction 36 399-406

[4]. Guo B H, Zou Y, Fang Y, Goh Y M and Zou P X 2021 Safety Sci. 135 105130

[5]. Arshad S, Akinade O, Bilal M and Bello S 2023 J. Build. Eng. 107049

[6]. Xu S, Wang J, Shou W, Ngo T, Sadick A M and Wang X 2021 Archi. Comput. Methods Eng. 28 3383-3397

[7]. Fang W, Love P E, Luo H and Ding L 2020 J. Advanced Engineering Informatics 43 100980

[8]. Liu W, Meng Q, Li Z and Hu X 2021 Build. 11(9) 409

[9]. Yan X and Kim Y C 2018 A conceptual framework of ITSMCA for a building collapse accident Eng. Constr. Archit. Ma. 25(6) 721-737

[10]. Ahn Y, Choi H and Kim B.S 2023 Development of early fire detection model for buildings using computer vision-based CCTV J. Build. Eng. 65 105647

[11]. Pushkar A, Senthilvel M and Varghese K 2018 Automated progress monitoring of masonry activity using photogrammetric point cloud ISARC 35 1-7

[12]. Luo H, Wang M, Wong P K Y and Cheng J C 2020 Full body pose estimation of construction equipment using computer vision and deep learning techniques AUTCON 110 103016.

[13]. Zhu D, Wen H and Deng Y 2020 Pro-active warning system for the crossroads at construction sites based on computer vision Eng. Constr. Archit. Ma. 27(5) 1145-1168

[14]. Wilkins J R 2011 Construction workers’ perceptions of health and safety training programmes Constr. Ma. Econom. 29(10) 1017-1026

[15]. Jeelani I, Albert A and Han K 2020 Improving Safety Performance in Construction Using Eye-Tracking, Visual Data Analytics, and Virtual Reality In Construction Research Congress 2020 American Society Civil Eng pp 395-404

[16]. Eiris R, Gheisari M and Esmaeili B 2018 PARS: Using augmented 360-degree panoramas of reality for construction safety training Int. J. Environ. Health. Res. 15(11) 2452

[17]. Fang W, Ding L, Luo H and Love P E 2018 AUTCON 91 53-61

[18]. Hayat A and Morgado-Dias F 2022 J. Deep learning-based automatic safety helmet detection system for construction safety. Applied Sciences. 12(16) 8268

[19]. Ding L, Fang W, Luo H, Love P E, Zhong B and Ouyang X 2018 J. A deep hybrid learning model to detect unsafe behavior: Integrating convolution neural networks and long short-term memory. AUTCON. 86 118-124

[20]. Alateeq M M, Fathimathul Rajeena P P and Ali M A 2023 Construction Site Hazards Identification Using Deep Learning and Computer Vision Sustainability 15(3) 2358

[21]. Yan X and Kim Y C 2018 A conceptual framework of ITSMCA for a building collapse accident Eng. Constr. Archit. Ma. 25(6) 721-737

[22]. Park M W and Brilakis I 2012 AUTCON 28 15-25

[23]. Park M.W, Palinginis E and Brilakis I 2012 Detection of construction workers in video frames for automatic initialization of vision trackers In Construction Research Congress 2012: Construction Challenges in a Flat World pp 940-949

[24]. Park M W and Brilakis I 2016 AUTCON 72 129-142.

[25]. Liu W, Shao Y, Zhai S, Yang Z and Chen P 2023 IEICE Transactions on Information and Systems 106(5) 653-661.

[26]. Azar E.R 2016 Construction equipment identification using marker-based recognition and an active zoom camera J. Constr. Eng. Ma. 30(3) 04015033

[27]. Xiao B and Kang S C 2021 Vision-based method integrating deep learning detection for tracking multiple construction machines J. Constr. Eng. Ma. 35(2) 04020071

[28]. Zhang M, Cao Z, Yang Z and Zhao X 2020 J. Constr. Eng. Ma. 146(6) 04020051

[29]. Zhang M and Ge S 2022 Vision and trajectory–Based dynamic collision prewarning mechanism for tower cranes J. Constr. Eng. Ma. 148(7) 04022057

[30]. Tsuchiya K and Ishigami G 2020 Vision-based measurement of spatio-temporal deformation of excavated soil for the estimation of bucket resistive force J. Terramechanics 90 11-21

[31]. Naghshbandi S N, Varga L and Hu Y 2021 J. Technologies for safe and resilient earthmoving operations: A systematic literature review AUTCON 125 103632

[32]. Goodrum P M, Zhai D and Yasin M F 2009 J. Relationship between changes in material technology and construction productivity J. Constr. Eng. Ma. 135(4) 278-287

[33]. Dimitrov A and Golparvar-Fard M 2014 J. Adv. Eng. Inform. 28(1) 37-49

[34]. Mahami H, Ghassemi N, Darbandy M T, Shoeibi A, Hussain S, Nasirzadeh F, Alizadehsani R, Nahavandi D, Khosravi A and Nahavandi S 2020 J. arXiv preprint arXiv 16344

[35]. Dinh T H, Ha Q P and La H M 2016 Computer vision-based method for concrete crack detection. Int. Control Automation Robotics & Vision (ICARCV) 1-6

[36]. Koch C, Georgieva K, Kasireddy V, Akinci B and Fieguth P 2015. Adv. Eng. Inform. 29(2) 196-210

[37]. Deng J, Singh A, Zhou Y, Lu Y and Lee V C S 2022 Constr Build Mater. 356 129238

Cite this article

Wang,Y. (2024). Research on computer vision application for safety management in construction. Theoretical and Natural Science,30,232-242.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Volume title: Proceedings of the 3rd International Conference on Computing Innovation and Applied Physics

Conference website: https://www.confciap.org/
ISBN:978-1-83558-283-1(Print) / 978-1-83558-284-8(Online)
Conference date: 27 January 2024
Editor:Yazeed Ghadi
Series: Theoretical and Natural Science
Volume number: Vol.30
ISSN:2753-8818(Print) / 2753-8826(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).