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Segmentation Tool for Images of Cracks

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Advances in Information Technology in Civil and Building Engineering (ICCCBE 2022)

Part of the book series: Lecture Notes in Civil Engineering ((LNCE,volume 357))

Abstract

Safety-critical infrastructures, such as bridges, are periodically inspected to check for existing damage, such as fatigue cracks and corrosion, and to guarantee the safe use of the infrastructure. Visual inspection is the most frequent type of general inspection, despite the fact that its detection capability is rather limited, especially for fatigue cracks. Machine learning algorithms can be used for augmenting the capability of classical visual inspection of bridge structures, however, the implementation of such an algorithm requires a massive annotated training dataset, which is time-consuming to produce. This paper proposes a semi-automatic crack segmentation tool that eases the manual segmentation of cracks on images needed to create a training dataset for machine learning algorithm. Also it can be used to measure the geometry of the crack. This tool makes use of an image processing algorithm, which was initially developed for the analysis of vascular systems on retinal images. The algorithm relies on a multi-orientation wavelet transform, which is applied to the image to construct the so-called ‘orientation scores’, i.e. a modified version of the image. Afterwards, the filtered orientation scores are used to formulate an optimal path problem that identifies the crack. The globally optimal path between manually selected crack endpoints is computed, using a state-of-the-art geometric tracking method. The pixel-wise segmentation is done afterwards using the obtained crack path. The proposed method outperforms fully automatic methods and shows potential to be an adequate alternative to the manual data annotation.

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Acknowledgement

Authors would like to thank the Dutch bridge infrastructure owners “ProRail” and “Rijkswaterstaat” for their support. The research is primarily funded by the Eindhoven Artificial Intelligence Systems Institute, and partly by the Dutch Foundation of Science NWO (Geometric learning for Image Analysis, VI.C 202-031).

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Authors and Affiliations

  1. Department of the Built Environment, Eindhoven University of Technology, Eindhoven, The Netherlands

    Andrii Kompanets, Davide Leonetti & H. H. Snijder

  2. Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands

    Remco Duits & Nicky van den Berg

  3. Eindhoven Artificial Intelligence Systems Institute, Eindhoven University of Technology, Eindhoven, The Netherlands

    Andrii Kompanets, Remco Duits & Davide Leonetti

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  1. Andrii Kompanets
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  2. Remco Duits
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  3. Davide Leonetti
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  4. Nicky van den Berg
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  5. H. H. Snijder
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Correspondence to Andrii Kompanets .

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Editors and Affiliations

  1. Department of Civil Engineering, University of Cape Town, Rondebosch, South Africa

    Sebastian Skatulla

  2. Department of Civil Engineering, University of Cape Town, Rondebosch, South Africa

    Hans Beushausen

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Kompanets, A., Duits, R., Leonetti, D., van den Berg, N., Snijder, H.H. (2024). Segmentation Tool for Images of Cracks. In: Skatulla, S., Beushausen, H. (eds) Advances in Information Technology in Civil and Building Engineering. ICCCBE 2022. Lecture Notes in Civil Engineering, vol 357. Springer, Cham. https://doi.org/10.1007/978-3-031-35399-4_8

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  • DOI: https://doi.org/10.1007/978-3-031-35399-4_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-35398-7

  • Online ISBN: 978-3-031-35399-4

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