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Concrete surface crack detection with the improved pre-extraction and the second percolation processing methods

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  • Zhong Qu
  • Fang-Rong Ju
  • Yang Guo
  • Ling Bai
  • Kuo Chen

Abstract

Monitoring the instantaneous and changing concrete surface condition is paramount to cost-effectively managing tunnel assets. In practice, detecting cracks efficiently and accurately is a very challenging task due to concrete blebs, stains, and illumination over the concrete surface. Unclear and tiny cracks cannot be detected effectively. In this paper, we proposed an ultra-efficient crack detection algorithm (CrackHHP) and an improved pre-extraction and second percolation process based on the percolation model to address these issues. Our contributions are shown as follows: 1) apply the overlapping grids and weight-based, redefined pixel value to obtain the candidate dark pixel image while preserving the cracks. 2) introduce the second percolation processing to generate a high-accuracy crack detection algorithm, which can connect the tiny fractures and detect the tiny cracks. 3) construct a high-efficiency and high-accuracy crack detection algorithm combining the improved pre-extraction and the second percolation process. The experimental results demonstrate that CrackHHP can significantly improve the efficiency and accuracy of crack detection.

Suggested Citation

  • Zhong Qu & Fang-Rong Ju & Yang Guo & Ling Bai & Kuo Chen, 2018. "Concrete surface crack detection with the improved pre-extraction and the second percolation processing methods," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-14, July.
    • Handle: RePEc:plo:pone00:0201109
    DOI: 10.1371/journal.pone.0201109
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    Cited by:

    1. Rachel Cohen & Geoff Fernie & Atena Roshan Fekr, 2020. "A Vision-Based Approach for Sidewalk and Walkway Trip Hazards Assessment," IJERPH, MDPI, vol. 17(22), pages 1-18, November.

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