Journal
AUTOMATION IN CONSTRUCTION
Volume 122, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.autcon.2020.103492
Keywords
Crack discretisation; Noise tolerance; Accurate crack localization; Pavement distress; Crack repair system; CrackIT
Funding
- UK Engineering and Physical Sciences Research Council (EPSRC) [EP/NO10523/1]
- Alan Turing Institute fellowship
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The new approach combines block-based and pixel-based techniques, improving crack detection accuracy while maintaining noise tolerance.
A variety of civil engineering applications require the identification of cracks in roads and buildings. In such cases, it is frequently helpful for the precise location of cracks to be identified as labelled parts within an image to facilitate precision repair for example. CrackIT is known as a crack detection algorithm that allows a user to choose between a block-based or a pixel-based approach. The block-based approach is noise-tolerant but is not accurate in edge localization while the pixel-based approach gives accurate edge localisation but is not noise-tolerant. We propose a new approach that combines both techniques and retains the advantages of each. The new method is evaluated on three standard crack image datasets. The method was compared with the CrackIT method and three deep learning methods namely, HED, RCF and the FPHB. The new approach outperformed the existing arts and reduced the discretisation errors significantly while still being noise-tolerant.
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