Automated Detection and Classification of Infiltration in Sewer Pipes
Publication: Journal of Infrastructure Systems
Volume 11, Issue 3
Abstract
Many researchers have reported on the poor status of sewer pipes in Canadian cities, revealing the presence of a number of defects that impact their performance. Inadequate inspection is considered one of the main causes behind the declining condition of this class of pipes. This could be attributed to the high cost of inspection and inadequate funds allocated to this purpose. The labor-intensive current inspection practice gives rise to such high costs. This paper presents an automated system “AUTO-DETECT” that detects and classifies defects in sewer pipes automatically. The paper expands on earlier developments made by the writers, provides an overview of the overall configuration of the system, and focuses primarily on automated detection and classification of infiltration in sewer pipes. Infiltration is considered to be a serious and common defect that contributes to an undesirable extra flow of about 40% in this class of pipes. The system utilizes pattern recognition, image analysis techniques, and artificial intelligence to perform its task. A case example is presented to demonstrate the use and capabilities of the developed system.
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Acknowledgments
The cooperation of Mr. Gilles St. Denis is highly appreciated. The financial support provided by the Natural Science and Engineering Research Council of Canada is greatly acknowledged.NRC
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Information & Authors
Information
Published In
Journal of Infrastructure Systems
Volume 11 • Issue 3 • September 2005
Pages: 165 - 171
Copyright
© 2005 ASCE.
History
Received: Oct 23, 2002
Accepted: Dec 27, 2004
Published online: Sep 1, 2005
Published in print: Sep 2005
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