The National Science Foundation (NSF) has estimated the total US investment in civil infrastructure at $US 20 trillion. The investments in sewer pipeline collection systems represent a major component of this figure. Recent studies have shown that to upgrade or restore municipal infrastructure in North America to an acceptable level the cost will be in excess of $US 100 billion. Many of these systems are eroding due to aging, excessive demand, misuse, poor construction, mismanagement, and neglect. Visual
inspection based on
Closed Circuit Television (CCTV) surveys is used widely in North America to assess the condition of underground sewer pipes. The human eye is extremely effective at recognition and classification, but it is not suitable for assessing pipe defects in thousand of miles of pipeline due to fatigue, subjectivity, and cost. Automatic recognition of various pipe defects is of considerable interest since it has the potential to overcome problems of fatigue, subjectivity, and ambiguity, leading to economic benefits.
In this paper, we present a system for the application of computer vision techniques to the automatic assessment of the structural condition of underground sewer pipes that overcomes the inherent limitations of existing digitizing paradigms. An image enhancement method that eliminates the non-uniform background illumination while ensuring that the enhanced image will be faithful to the source image and enables quantification of distress features is presented. Algorithms for analysis of joint and lateral recognition are also presented and should allow engineers to be able to gain the goal of a fully automated underground sewer defect detection system based on digital image processing of SSET surveys according to the principles demonstrated in this paper.
