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A Vision-Based Method for Automatic Crack Detection in Railway Sleepers

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Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017 (CORES 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 578))

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Abstract

In this paper, a method for automatic selection and classification of the sleeper cracks is presented. This method includes three main sequential steps of image pre-processing, sleeper detection and crack detection. Two approaches including rule-based method and template matching method in the frequency domain are proposed for the sleeper detection step. We utilize adaptive threshold binarization to handle challenging crack detection under non-uniform lightening condition and hierarchical structure for the decision making step. Two unsupervised classifiers are exploited to detect the cracks. The results show that the presented method has the overall detection rate with accuracy of at least 87 percent.

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Author information

Authors and Affiliations

  1. Pattern Recognition Group, University of Siegen, Siegen, Germany

    Ahmad Delforouzi, Amir Hossein Tabatabaei, Muhammad Hassan Khan & Marcin Grzegorzek

Authors
  1. Ahmad Delforouzi
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  2. Amir Hossein Tabatabaei
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  3. Muhammad Hassan Khan
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  4. Marcin Grzegorzek
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Corresponding author

Correspondence to Ahmad Delforouzi .

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Editors and Affiliations

  1. Department of Systems and Computer Networks, Wrocław University of Technology, Wrocław, Poland

    Marek Kurzynski

  2. Department of Systems and Computer Networks, Wrocław University of Technology, Wroclaw, Poland

    Michal Wozniak

  3. Department of Systems and Computer Networks, Wrocław University of Technology , Wroclaw, Poland

    Robert Burduk

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Delforouzi, A., Tabatabaei, A.H., Khan, M.H., Grzegorzek, M. (2018). A Vision-Based Method for Automatic Crack Detection in Railway Sleepers. In: Kurzynski, M., Wozniak, M., Burduk, R. (eds) Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017. CORES 2017. Advances in Intelligent Systems and Computing, vol 578. Springer, Cham. https://doi.org/10.1007/978-3-319-59162-9_14

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  • DOI: https://doi.org/10.1007/978-3-319-59162-9_14

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