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Morphological segmentation and classification of underground pipe images

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Abstract

Visual inspection based on closed circuit television surveys is used widely in North America to assess the condition of underground pipes. Although the human eye is extremely effective at recognition and classification, it is not suitable for assessing pipe defects in thousand of miles of pipeline because of fatigue, subjectivity, and cost. In this paper, simple, robust, and efficient image segmentation and classification algorithm for the automated analysis of scanned underground pipe images is presented. The experimental results demonstrate that the proposed algorithm can precisely segment and classify pipe cracks, holes, laterals, joints and collapse surface from underground pipe images

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Pennsylvania State University, University Park, 16801, PA, USA

    Sunil K. Sinha

  2. Department of Systems Design Engineering, University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada

    Paul W. Fieguth

Authors
  1. Sunil K. Sinha
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  2. Paul W. Fieguth
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Correspondence to Sunil K. Sinha.

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Sinha, S.K., Fieguth, P.W. Morphological segmentation and classification of underground pipe images. Machine Vision and Applications 17, 21–31 (2006). https://doi.org/10.1007/s00138-005-0012-0

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  • DOI: https://doi.org/10.1007/s00138-005-0012-0

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