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Apr 26, 2012
Automatic Defect Detection and Recognition for Asset Condition Assessment: A Case Study on Sewer Pipeline Infrastructure System
Authors: W. Guo [email protected], L. Soibelman [email protected], and J. H. Garrett, Jr. [email protected]Author Affiliations
Publication: Computing in Civil Engineering (2007)
Abstract
This paper presents the preliminary results of a research project that seeks to investigate the use of advanced image processing, computer vision and pattern recognition technologies, to facilitate asset inspection and condition assessment of sewer pipeline infrastructure. This research aims to provide a basis for sewer pipeline inspection and condition assessment, enabling automatic detection and recognition of defects and/or critical features from sewer pipeline inspection videos and images. An approach for automatic detection and recognition is presented in this paper. The major steps of the approach are illustrated by examples using actual sewer inspection visual data.
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© 2007 American Society of Civil Engineers.
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Published online: Apr 26, 2012
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Graduate Research Assistant, Department of Civil and Environmental Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213. E-mail: [email protected]
Associate Professor, Department of Civil and Environmental Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213. E-mail: [email protected]
Professor & Department Head, Department of Civil and Environmental Engineering, Carnegie Mellon University; 5000 Forbes Avenue, Pittsburgh, PA 15213. E-mail: [email protected]
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