Computer vision applications in offsite construction

被引：24
作者：
Alsakka, Fatima [1 ]
Assaf, Sena [1 ]
El-Chami, Ibrahim [2 ]
Al -Hussein, Mohamed [1 ]
机构：
[1] Univ Alberta, 9211 116 St NW, Edmonton, AB, Canada
[2] Univ British Columbia, Vancouver, BC, Canada
来源：
AUTOMATION IN CONSTRUCTION | 2023年 / 154卷
关键词：
Scoping review; Offsite construction; Artificial intelligence; Computer vision; Custom vision; Object detection; Object tracking; Edge detection; Feature extraction; 3D reconstruction; Segmentation; EQUIPMENT; SYSTEM; RECOGNITION;
D O I：
10.1016/j.autcon.2023.104980
中图分类号：
TU [建筑科学];
学科分类号：
0813 ;
摘要：
The field of computer vision has undergone rapid growth in recent years, yet the use of computer vision in offsite construction remains an under-researched area of study. Given the current momentum around the adoption of this technology, this article presents a scoping review of computer vision applications in offsite construction. It provides (1) summaries of and discussions on the research areas in which computer vision is used in offsite construction, the computer vision tasks undertaken, the algorithms used, and related performance evaluation results and limitations, (2) a tabulated summary of performance-related terms commonly used in computer vision applications (to facilitate understanding of the performance evaluation results reported in the review), and (3) potential avenues of future research. The review provides a useful point of reference for practitioners and researchers in the offsite construction industry, aiding their understanding of current practice, limitations, research gaps, and potential opportunities to apply computer vision.
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页数：20
相关论文
共 113 条
  • [21] Chu W., 2019, P 36 INT S AUT ROB C, P722, DOI [10.22260/isarc2019/0097, DOI 10.22260/ISARC2019/0097]
  • [22] Monocular Vision-Based Framework for Biomechanical Analysis or Ergonomic Posture Assessment in Modular Construction
    Chu, Wenjing
    Han, Sanghyeok
    Luo, Xiaowei
    Zhu, Zhenhua
    [J]. JOURNAL OF COMPUTING IN CIVIL ENGINEERING, 2020, 34 (04)
  • [23] cocodataset, 2022, COCO COMM OBJ CONT
  • [24] Management's perception of key performance indicators for construction
    Cox, RF
    Issa, RRA
    Ahrens, D
    [J]. JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT-ASCE, 2003, 129 (02): : 142 - 151
  • [25] Das A, 2018, P 2018 INT C SMART C, P1
  • [26] Data Bridge Market Research, 2022, GLOB COMP VIS MARK I
  • [27] Enhancing the scoping study methodology: a large, inter-professional team's experience with Arksey and O'Malley's framework
    Daudt, Helena M. L.
    van Mossel, Catherine
    Scott, Samantha J.
    [J]. BMC MEDICAL RESEARCH METHODOLOGY, 2013, 13
  • [28] Engineering Approach Using ANN to Improve and Predict Construction Labor Productivity under Different Influences
    El-Gohary, Khaled Mahmoud
    Aziz, Remon Fayek
    Abdel-Khalek, Hesham A.
    [J]. JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT, 2017, 143 (08)
  • [29] A deep learning-based approach for mitigating falls from height with computer vision: Convolutional neural network
    Fang, Weili
    Zhong, Botao
    Zhao, Neng
    Love, Peter Ed
    Luo, Hanbin
    Xue, Jiayue
    Xu, Shuangjie
    [J]. ADVANCED ENGINEERING INFORMATICS, 2019, 39 : 170 - 177
  • [30] Automated detection of workers and heavy equipment on construction sites: A convolutional neural network approach
    Fang, Weili
    Ding, Lieyun
    Zhong, Botao
    Love, Peter E. D.
    Luo, Hanbin
    [J]. ADVANCED ENGINEERING INFORMATICS, 2018, 37 : 139 - 149
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