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Computer vision-based construction progress monitoring
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AI
DOI：10.1016/j.autcon.2022.104245.png)
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摘要
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Automating the process of construction progress monitoring through computer vision can enable effective control of projects. Systematic classification of available methods and technologies is necessary to structure this complex, multi-stage process. Using the PRISMA framework, relevant studies in the area were identified. The various concepts, tools, technologies, and algorithms reported by these studies were iteratively categorised, developing an integrated process framework for Computer-Vision-Based Construction Progress Monitoring (CVCPM). This framework comprises: data acquisition and 3D-reconstruction, as-built modelling, and progress assessment. Each stage is discussed in detail, positioning key studies, and concurrently comparing the methods used therein. The four levels of progress monitoring are defined and found to strongly influence all stages of the framework. The need for benchmarking CV-CPM pipelines and components are discussed, and potential research questions within each stage are identified. The relevance of CV-CPM to support emerging areas such as Digital Twin is also discussed.
Keyword:
Progress monitoring
Computer vision
Automated construction
Data acquisition
3D reconstruction
As-built modelling
Point cloud
Scan to BIM
Literature review
Digital Twin
期刊
IF:
11.5
论文数:
6.2K
被引数:
4.2W
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