|Table of Contents|

Multi-level clustering algorithm for crack detection of concrete surface(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2013年06期
Page:
7-13
Research Field:
道路与铁道工程
Publishing date:

Info

Title:
Multi-level clustering algorithm for crack detection of concrete surface
Author(s):
DONG An-guo;  ZHANG Xian-yan;  XUE Hong-zhi;  SONG Jun
School of Science, Chang'an University, Xi'an 710064, Shaanxi, China
Keywords:
pavement engineering;  crack detection;  image processing;  k-means clustering;  spectral clustering
PACS:
U416.216
DOI:
-
Abstract:
In order to detect the crack and its width of concrete surface, the k-means clustering was applied for crack digital image, and binary image was got based on taking out entire suspected crack pixels from clustering results. The connected components of binary image were extracted according to the ubiety of pixels, the distance function of connected components was constructed considering connected components as clustering objects. Connected components could be clustered by spectral clustering algorithm, pseudo cracks were removed on the basis of crack features, and whole crack image was obtained. Numerical calculations of crack width were carried out twice by local rotation algorithm. Research result shows that multi-level clustering algorithm can get rid of more noises during extracting crack, and has stronger anti-noise ability compared with Canny operator and Sobel operator. When the crack width is calculated by local rotation algorithm, the average relative errors of calculated value and actual value are 3.86% and 2.40% respectively, so the algorithm has high accuracy and can be used for width calculations of all kinds of cracks. 3 tabs, 9 figs, 14 refs.

References:

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Last Update: 2013-12-20