Construction Site Safety Management: A Computer Vision and Deep Learning Approach

被引:25
作者
Lee, Jaekyu [1 ]
Lee, Sangyub [1 ]
机构
[1] Korea Elect Technol Inst, Energy IT Convergence Res Ctr, Seongnam 13509, South Korea
关键词
worker safety management; virtual datasets; synthetic datasets; image processing; transfer learning; virtual validation environment;
D O I
10.3390/s23020944
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
In this study, we used image recognition technology to explore different ways to improve the safety of construction workers. Three object recognition scenarios were designed for safety at a construction site, and a corresponding object recognition model was developed for each scenario. The first object recognition model checks whether there are construction workers at the site. The second object recognition model assesses the risk of falling (falling off a structure or falling down) when working at an elevated position. The third object recognition model determines whether the workers are appropriately wearing safety helmets and vests. These three models were newly created using the image data collected from the construction sites and synthetic image data collected from the virtual environment based on transfer learning. In particular, we verified an artificial intelligence model based on a virtual environment in this study. Thus, simulating and performing tests on worker falls and fall injuries, which are difficult to re-enact by humans, are efficient algorithm verification methods. The verification and synthesis data acquisition method based on a virtual environment is one of the main contributions of this study. This paper describes the overall application development approach, including the structure and method used to collect the construction site image data, structure of the training image dataset, image dataset augmentation method, and the artificial intelligence backbone model applied for transfer learning.
引用
收藏
页数:22
相关论文
共 39 条
[1]  
Adelson E. H., 1984, RCA ENG, P1
[2]   2D Human Pose Estimation: New Benchmark and State of the Art Analysis [J].
Andriluka, Mykhaylo ;
Pishchulin, Leonid ;
Gehler, Peter ;
Schiele, Bernt .
2014 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2014, :3686-3693
[3]  
Azhar S., 2013, 49 ASC ANN INT C P
[4]  
Balakreshnan Balamurugan, PROCEDIA MANUFACTURI, P277, DOI DOI 10.1016/J.PROMFG.2020.04.017
[5]   THREE-DIMENSIONAL OBJECT RECOGNITION. [J].
Besl, Paul J. ;
Jain, Ramesh C. .
Computing surveys, 1985, 17 (01) :75-145
[6]  
Bradski G., 2008, Learning OpenCV: Computer vision with the OpenCV library, DOI DOI 10.1109/MRA.2009.933612
[7]   A Vision-Based Approach for Ensuring Proper Use of Personal Protective Equipment (PPE) in Decommissioning of Fukushima Daiichi Nuclear Power Station [J].
Chen, Shi ;
Demachi, Kazuyuki .
APPLIED SCIENCES-BASEL, 2020, 10 (15)
[8]   Color image segmentation: advances and prospects [J].
Cheng, HD ;
Jiang, XH ;
Sun, Y ;
Wang, JL .
PATTERN RECOGNITION, 2001, 34 (12) :2259-2281
[9]   Detection of Personal Protective Equipment (PPE) Compliance on Construction Site Using Computer Vision Based Deep Learning Techniques [J].
Delhi, Venkata Santosh Kumar ;
Sankarlal, R. ;
Thomas, Albert .
FRONTIERS IN BUILT ENVIRONMENT, 2020, 6
[10]  
Deng J, 2009, PROC CVPR IEEE, P248, DOI 10.1109/CVPRW.2009.5206848