@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 = {<p>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.</p>
},
	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}
}
