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

  • Ahmad Delforouzi
  • Amir Hossein Tabatabaei
  • Muhammad Hassan Khan
  • Marcin Grzegorzek
Conference paper

DOI: 10.1007/978-3-319-59162-9_14

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

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.

Keywords

Sleeper crack detection Adaptive thresholding Support vector machines Template matching 

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Ahmad Delforouzi
    • 1
  • Amir Hossein Tabatabaei
    • 1
  • Muhammad Hassan Khan
    • 1
  • Marcin Grzegorzek
    • 1
  1. 1.Pattern Recognition GroupUniversity of SiegenSiegenGermany

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