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Deep learning-based object detection for dynamic construction site management
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AI
DOI：10.1016/j.autcon.2024.105494.png)
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Abstract
En 中文
Deep learning-based object detection (DLOD) methods are crucial in assisting dynamic construction site management (DCSM) but have not been systematically studied. This paper investigates DLOD methods through a mixed-methods systematic review concerning concept, methodology, and value dimensions. This review analyses 110 DLOD cases identified from 103 journal papers published during 2014-2023 by examining what are the main targets and focuses, what algorithms and techniques are adopted, and how do DLOD methods perform in practical application scenarios of DCSM. Threefold knowledge gaps are identified as multifaceted concept with singularity and oversimplification, progressive methodology with a weak data foundation, and deterministic value with subjective evaluation and limited scenarios. Three directions are proposed for developing advanced DLOD methods: develop large-scale, high-quality, open-source construction datasets, adopt advanced algorithms, and explore integrated DCSM scenarios. This paper marks a milestone of DLOD research and sheds light on its future development for DCSM.
Keywords:
Dynamic construction site management
Object detection
Deep learning
Systems approach
Computer vision
Journal
IF:
11.5
Papers:
6.2K
Citations:
4.2W