Automated Condition Assessment of Buried Sewer Pipeline Using Computer Vision Techniques

Closed Circuit Television (CCTV) surveys are used widely in North America to assess the structural integrity of underground pipes. The video images are examined visually and classified into grades according to degrees of damage. 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 images due to fatigue, subjectivity, and cost. This paper presents ongoing research into the automatic assessment of the structural condition of underground pipes for the purpose of preventive maintenance by municipalities. A simple, robust and efficient image-processing algorithm for the automated analysis of underground pipe scanned images is presented. The algorithm consists of gray-scale conversion followed by a sequence of morphological operations to accurately extract pipe joints and laterals (where a smaller pipe is connecting to the main). The extraction of joints and laterals is a crucial step in the detection of surface cracks in pipes, which can be analysed by subsequent filtering operation. The proposed approach can be completely automated and has been tested on over one hundred scanned images of underground pipes from major cities in North America.