• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus
  • Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
  • Chinese S&T Journal Citation Reports
  • Chinese Science Citation Database
XIE Mingzhi, FAN Dingmeng, JIANG Zhipeng, DENG Fei, WANG Kun, HAN Chen, YANG Yongqing. Research Status and Prospects of Computer Vision-Based Crack Detection of Concrete Structure[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20240115
Citation: XIE Mingzhi, FAN Dingmeng, JIANG Zhipeng, DENG Fei, WANG Kun, HAN Chen, YANG Yongqing. Research Status and Prospects of Computer Vision-Based Crack Detection of Concrete Structure[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20240115

Research Status and Prospects of Computer Vision-Based Crack Detection of Concrete Structure

doi: 10.3969/j.issn.0258-2724.20240115
  • Received Date: 08 Mar 2024
  • Rev Recd Date: 11 Jul 2024
  • Available Online: 29 Mar 2025
  • As one of the important contents of health monitoring of concrete structure, crack detection reflects the stress and damage state of the structure, and the detection and evaluation is the core technology to ensure structure safety for service. The traditional detection methods have limited coverage in time and space and are greatly affected by environmental and altitude factors, so the detection efficiency and accuracy are relatively low. Additionally, they are dependent on subjective judgment, which is easy to cause missed detection and false detection. The detection method based on computer vision is equipped with digital imaging equipment for data acquisition, input, and image processing to automatically analyze and identify the concrete surface, which has the advantages of high efficiency, accuracy, and objectivity and is widely used in the field of intelligent crack detection of concrete structures. The principle, method, and application of concrete crack detection based on computer vision were described in detail from four aspects: image acquisition, image processing, recognition algorithm, and structure evaluation. Besides, the application of crack image acquisition equipment and various image preprocessing methods in digital imaging technology was reviewed comprehensively, and the advantages, disadvantages, and applicability of different recognition algorithms were analyzed. At the same time, the shortcomings of current research were summarized, and the challenges and problems faced by the application of computer vision technology for equipment intelligence and lightweight network were analyzed. Then the corresponding solutions were proposed. Prospects are also presented from the aspects of multi-source data fusion and utilization, lightweight intelligent equipment, digital imaging and crack mapping, and high-efficiency and real-time structure evaluation.

     

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