@article {385, title = {Automated detection of crack defects in buried concrete pipe images}, journal = {Automation in Construction}, volume = {15}, year = {2006}, pages = {58 - 72}, abstract = {

The detection of cracks in concrete infrastructure is a problem of great interest. In particular, the detection of cracks in buried pipes is a crucial step in assessing the degree of pipe deterioration for municipal and utility operators. The key challenge is that whereas joints and laterals have a predictable appearance, the randomness and irregularity of cracks make them difficult to model. Our previous work has led to a segmented pipe image (with holes, joints, and laterals eliminated) obtained by a morphological approach. This paper presents the development of a statistical filter for the detection of cracks in the pipes. We propose a two-step approach. The first step is local and is used to extract crack features from the buried pipe images; we present two such detectors as well as a method for fusing them. The second step is global and defines the cracks among the segment candidates by processes of cleaning and linking. The influences of the parameters on crack detection are studied and results are presented for various pipe images.

}, keywords = {Automated inspection, Crack detection, image processing, Pipeline assessment, Pipeline Infrastructure, Segmentation, Statistical properties}, issn = {0926-5805}, doi = {http://dx.doi.org/10.1016/j.autcon.2005.02.006}, author = {S K. Sinha and P Fieguth} } @article {388, title = {Neuro-fuzzy network for the classification of buried pipe defects}, journal = {Automation in Construction}, volume = {15}, year = {2006}, pages = {73 - 83}, abstract = {

Pipeline infrastructure is decaying at an accelerating rate due to reduced funding and insufficient quality control resulting in poor installation, little or no inspection and maintenance, and a general lack of uniformity and improvement in design, construction and operation practices. The current practice that is being followed to inspect the conditions of pipes is usually time consuming, tedious and expensive. It may also lead to diagnostic errors due to lack of concentration of human operators. Buried pipe defect classification is thus a practical and important pattern classification problem. These defects appear in the form of randomly shaped cracks and holes, broken joints and laterals, and others. This paper proposes a new neuro-fuzzy classifier that combines neural networks and concepts of fuzzy logic for the classification of defects by extracting features in segmented buried pipe images. A comparative evaluation of the K-NN, fuzzy K-NN, conventional backpropagation network, and proposed neuro-fuzzy projection network classifiers is carried out. Among the five neural methods implemented and tested, the proposed neuro-fuzzy classifier performs the best, with classification accuracies around 90\% on real concrete pipe images.

}, keywords = {Automated inspection, Backpropagation network, Featuresm Neural Network, image processing, Neuro-fuzzy projection network, Pipeline Infrastructure, Segmentation}, issn = {0926-5805}, doi = {http://dx.doi.org/10.1016/j.autcon.2005.02.005}, author = {S K. Sinha and P Fieguth} } @article {391, title = {Segmentation of buried concrete pipe images}, journal = {Automation in Construction}, volume = {15}, year = {2006}, pages = {47 - 57}, abstract = {

The enormity of the problem of deteriorating pipeline infrastructure is widely apparent. Since a complete rebuilding of the piping system is not financially realistic, municipal and utility operators require the ability to monitor the condition of buried pipes. Thus, reliable pipeline assessment and management tools are necessary to develop long term cost effective maintenance, repair, and rehabilitation programs. In this paper a simple, robust and efficient image segmentation algorithm for the automated analysis of scanned underground pipe images is presented. The algorithm consists of image pre-processing followed by a sequence of morphological operations to accurately segment pipe cracks, holes, joints, laterals, and collapsed surfaces, a crucial step in the classification of defects in underground pipes. The proposed approach can be completely automated and has been tested on five hundred scanned images of buried concrete sewer pipes from major cities in North America.

}, keywords = {Automated inspection, image processing, Mathematical morphology, Pipeline assessment, Pipeline Infrastructure, Segmentation}, issn = {0926-5805}, doi = {http://dx.doi.org/10.1016/j.autcon.2005.02.007}, author = {S K. Sinha and P Fieguth} }