Abdellatif, M orcid.org/0000-0002-7641-4723, Peel, H orcid.org/0000-0002-9432-3438, Cohn, AG orcid.org/0000-0002-7652-8907 et al. (1 more author) (2021) Combining Block-based and Pixel-based Approaches to Improve Crack Detection and Localisation. Automation in Construction, 122. 103492. ISSN 0926-5805
Abstract
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | ©2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) |
Keywords: | Crack discretisation; Noise tolerance; Accurate crack localization; Pavement distress; Crack repair system; CrackIT |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Civil Engineering (Leeds) The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Funding Information: | Funder Grant number Alan Turing Institute No ref given EPSRC (Engineering and Physical Sciences Research Council) EP/N010523/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 25 Nov 2020 15:33 |
Last Modified: | 08 Jan 2025 12:52 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.autcon.2020.103492 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:168305 |