混凝土表面裂缝检测的多级聚类算法

董安国; 张仙艳; 薛宏智; 宋君

混凝土表面裂缝检测的多级聚类算法

长安大学理学院，陕西西安 710064

基金项目:

国家自然科学基金项目 11171043

中央高校基本科研业务费专项资金项目 CHD2011JC010

中央高校基本科研业务费专项资金项目 CHD2012TD015

详细信息

作者简介:
董安国(1964-), 男, 浙江象山人, 长安大学教授, 从事图像处理研究

中图分类号: U416.216
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- 被引次数: 0
出版历程
- 收稿日期: 2013-07-01
- 刊出日期: 2013-12-25

Multi-level clustering algorithm for crack detection of concrete surface

School of Science, Chang'an University, Xi'an 710064, Shaanxi, China

More Information

Author Bio:
DONG An-guo(1964-), male, professor, +86-29-82334890, donganguo@chd.edu.cn

摘要

摘要: 为了检测混凝土表面裂缝及其宽度, 对含有裂缝的数字图像进行k-均值聚类, 提取出所有疑似裂缝的像素点, 进行了二值化处理。根据像素点的位置关系, 提取连通分量, 将连通分量作为聚类对象, 构造连通分量间的距离函数。利用谱聚类算法将连通分量聚类, 根据裂缝特征, 去掉伪裂缝部分, 得到完整的裂缝对象, 并通过局部旋转算法对裂缝的宽度进行了2次数值计算。分析结果表明: 与Canny、Sobel算子比较, 多级聚类算法在裂缝提取时能去掉较多的噪声, 抗噪能力强; 通过局部旋转算法计算裂缝宽度时, 计算值与实际值的平均相对误差分别为3.86%、2.40%, 算法精度高, 适用于各种类型裂缝宽度计算。
- 路面工程 /
- 裂缝检测 /
- 图像处理 /
- k-均值聚类 /
- 谱聚类
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.
- pavement engineering /
- crack detection /
- image processing /
- k-means clustering /
- spectral clustering

HTML全文

图 1 用相机拍摄到的裂缝

Figure 1. Cracks with camera

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图 2 聚类及二值化后的裂缝

Figure 2. Clustering and binary cracks

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图 3 用谱聚类提取后的裂缝

Figure 3. Cracks based on spectral clustering

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图 4 算子提取裂缝

Figure 4. Cracks based on operators

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图 5 复杂背景裂缝检测过程及方法对比

Figure 5. Process of crack detection with complicated background and method comparison

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图 6 裂缝示意

Figure 6. Crack sketch

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图 7 像素点拟合直线

Figure 7. Fitting line of pixels

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图 8 旋转后裂缝

Figure 8. Crack after rotating

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图 9 混凝土表面裂缝

Figure 9. Concrete surface cracks

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表 1 裂缝像素点坐标

Table 1. Coordinates of crack pixels

下载: 导出CSV

表 2 图 9(a)裂缝实际宽度与计算宽度

Table 2. Actual widths and calculated widths of crack in Fig. 9(a)

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表 3 图 9(b)裂缝实际宽度与计算宽度

Table 3. Actual widths and calculated widths of crack in Fig. 9(b)

下载: 导出CSV

参考文献(14)

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