Visual Pattern Recognition Supporting Defect Reporting and Condition Assessment of Wastewater Collection Systems

Abstract

Computational approaches to visual data and information processing can assist asset management procedures. In this paper, we present a computerized visual pattern recognition approach to facilitate inspection and condition assessment of wastewater collection systems in realistic settings. This research aims to provide a basis for condition assessment and defect reporting in wastewater infrastructure, enabling automatic detection and recognition of defects and patterns from inspection images or videos. An approach for sensing and automatically detecting critical areas or regions of interest, and further discovering and recognizing objects of interest in large image data sets, is described. The major steps of the approach are illustrated by examples using actual inspection images acquired from wastewater pipelines.