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Computer Vision Techniques for Automatic Structural Assessment of Underground Pipes
Author(s) -
Sinha Sunil K.,
Fieguth Paul W.,
Polak Maria A.
Publication year - 2003
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/1467-8667.00302
Subject(s) - pipeline (software) , preprocessor , automated x ray inspection , image processing , pipeline transport , computer science , machine vision , asset (computer security) , engineering , artificial intelligence , computer vision , engineering drawing , image (mathematics) , mechanical engineering , computer security
Pipeline surface defects such as cracks cause major problems for asset managers, particularly when the pipe is buried under the ground. The manual inspection of surface defects in the underground pipes has a number of drawbacks, including subjectivity, varying standards, and high costs. An automatic inspection system using image processing and artificial intelligence techniques can overcome many of these disadvantages and offer asset managers an opportunity to significantly improve quality and reduce costs. This article presents a system for the application of computer vision techniques to the automatic assessment of the structural condition of underground pipes. The algorithm consists of image preprocessing, a sequence of morphological operations to accurately extract pipe joints and laterals (where smaller pipe is connected to main bigger pipe), and statistical filters for detection of surface cracks in the pipeline network. The proposed approach can be completely automated and has been tested on over 1,000 scanned images of underground pipes from major cities in North America.