Automated Analysis of Sewer Inspection Closed Circuit Television Videos Using Image Processing Techniques

Abstract

Closed circuit television (CCTV) has been the primary sewer inspection method over four decades, and it is still widely used around the world. Sewer condition information is an important tool for developing asset management and renewal planning strategies. Several other sewer inspection technologies, such as digital side scanners, sonar, and laser-based scanning have rapidly advanced over the last two decades. However, these technologies still have limited use in the industry, and CCTV technology remains the most widely used sewer inspection technology. CCTV sewer inspections are highly dependent on the operator’s interpretation and assessment, and hence, could be somewhat subjective. Also, operators may be subject to fatigue due to lengthy inspection sessions, which could lead to erroneous assessment of the sewer condition. This research aims to develop algorithms and a software prototype to automate the analysis and assessment of sewer condition from CCTV videos using image processing and pattern recognition techniques. Several new algorithms are proposed to support automated identification of regions of interest (ROI) in the CCTV videos, classification of frames based on camera orientation, segmentation using grey-level intensity analysis, and automated detection of several sewer defects. Starting from a raw CCTV video, the system’s operation starts by performing camera motion analysis to identify and locate the ROI inside the sewer. These ROI represent `suspicious` video segments where sewer defects are more likely to be present. Frames within the ROI are processed and analyzed to extract useful information and identify existing defects, if any. A number of algorithms were developed to segment individual frames and to automatically detect and classify structural and operational defects. The system is composed of three main components. The first component was implemented in C++ using Intel’s Open Computer Vision library. This component provides users with a graphical interface that enables access to all functions provided by other software components. The second component comprises a set of MATLAB scripts that implement the segmentation and defect detection algorithms. The third component includes the SVMlight software. The system was built and tested using a set of CCTV videos obtained from the City of Regina, Canada