Automated detection of cracks in buried concrete pipe images

被引:183
作者
Sinha, SK [1 ]
Fieguth, PW
机构
[1] Penn State Univ, Dept Civil & Environm Engn, University Pk, PA 16802 USA
[2] Univ Waterloo, Dept Syst Design Engn, Waterloo, ON N2L 3G1, Canada
关键词
pipeline infrastructure; automated inspection; pipeline assessment; image processing; segmentation; crack detection; statistical properties;
D O I
10.1016/j.autcon.2005.02.006
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
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. (c) 2005 Elsevier B.V. All rights reserved.
引用
收藏
页码:58 / 72
页数:15
相关论文
共 25 条
[21]  
SINHA SK, 2000, THESIS U WATERLOO ON
[22]  
SINHA SK, IN PRESS AUTOM CONST
[23]   THE HOUGH TRANSFORM APPLIED TO SAR IMAGES FOR THIN LINE DETECTION [J].
SKINGLEY, J ;
RYE, AJ .
PATTERN RECOGNITION LETTERS, 1987, 6 (01) :61-67
[24]   BOUNDARY AND OBJECT DETECTION IN REAL WORLD IMAGES [J].
YAKIMOVSKY, Y .
JOURNAL OF THE ACM, 1976, 23 (04) :599-618
[25]   Edge linking by a directional potential function (DPF) [J].
Zhu, QM ;
Payne, M ;
Riordan, V .
IMAGE AND VISION COMPUTING, 1996, 14 (01) :59-70