Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Automated Defect Detection in Urban Wastewater Pipes Using Invariant Features Found in Video Images
This paper presents an innovative approach for condition assessment and defect reporting in wastewater infrastructure, enabling automatic detection of defects and patterns from inspection imagery in realistic settings. In this paper, we describe an automated three-step approach using local scale-orientation-illumination invariant features to detect surface defects and critical patterns from inspection imagery of wastewater pipelines. The invariant features were extracted by applying Scale Invariant Feature Transform (SIFT) algorithm developed by Lowe. The devised detection approach has been experimentally tested using a diverse collection of digital images acquired from real inspection scenarios. The evaluation results demonstrated that it is possible and feasible to use such an automated approach for defect reporting and condition assessment in wastewater pipe infrastructure.
Automated Defect Detection in Urban Wastewater Pipes Using Invariant Features Found in Video Images
This paper presents an innovative approach for condition assessment and defect reporting in wastewater infrastructure, enabling automatic detection of defects and patterns from inspection imagery in realistic settings. In this paper, we describe an automated three-step approach using local scale-orientation-illumination invariant features to detect surface defects and critical patterns from inspection imagery of wastewater pipelines. The invariant features were extracted by applying Scale Invariant Feature Transform (SIFT) algorithm developed by Lowe. The devised detection approach has been experimentally tested using a diverse collection of digital images acquired from real inspection scenarios. The evaluation results demonstrated that it is possible and feasible to use such an automated approach for defect reporting and condition assessment in wastewater pipe infrastructure.
Automated Defect Detection in Urban Wastewater Pipes Using Invariant Features Found in Video Images
Guo, W. (Autor:in) / Soibelman, L. (Autor:in) / Garrett, Jr., J. H. (Autor:in)
Construction Research Congress 2009 ; 2009 ; Seattle, Washington, United States
Building a Sustainable Future ; 1194-1203
01.04.2009
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Automated Defect Detection in Urban Wastewater Pipes Using Invariant Features Found in Video Images
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