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Infrastructures 2019, 4(1), 10; https://doi.org/10.3390/infrastructures4010010

Review on Computer Aided Sewer Pipeline Defect Detection and Condition Assessment

1
Department of Building, Civil and Environmental Engineering, Concordia University, Montreal, QC H3G 1M8, Canada
2
Department of Building and Real Estate (BRE), Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
*
Author to whom correspondence should be addressed.
Received: 8 January 2019 / Revised: 11 February 2019 / Accepted: 25 February 2019 / Published: 1 March 2019
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PDF [2054 KB, uploaded 1 March 2019]
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Abstract

Physical and operational inspection of sewer pipelines is critical to sustaining an acceptable level of system serviceability. Emerging inspection tools in addition to developments in sensor and lens technologies have facilitated sewer condition assessment and increased the quality and consistency of provided data. Meanwhile, sewer networks are too vast to be adequately investigated manually so the development of innovative computer vision techniques for automation applications has become an interest point of recent studies. This review paper presents the current state of inspection technology practices in sewer pipelines. An overall inspection tool comparison was conducted and the advantages and disadvantages of each method were discussed. This was followed by a comprehensive review of recent studies on visual inspection automation using computer vision and machine learning techniques. Finally, current achievements and limitations of existing automation methods were debated to outline open challenges and future research for both infrastructure management and computer science researchers. View Full-Text
Keywords: infrastructure; condition assessment; sewer networks; automated inspection infrastructure; condition assessment; sewer networks; automated inspection
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Moradi, S.; Zayed, T.; Golkhoo, F. Review on Computer Aided Sewer Pipeline Defect Detection and Condition Assessment. Infrastructures 2019, 4, 10.

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