基于多种连通域特征的结构表面裂缝提取方法
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摘要
针对传统裂缝图像信息提取方法的局限性,提出了一种基于多种连通域特征的工程结构表面裂缝提取方法.在采用最大类间方差(Otsu)法对原始图像进行初始分割的基础上,对裂缝图像的连通域面积、最小外接矩形长宽比和连通域内最远距离等连通域特征参数分别进行K-means聚类分析,得到了裂缝目标和噪声背景区域的连通域特征参数分布范围;进而建立了一种新的裂缝信息提取方法,并给出了具体算法流程.该方法与其他方法对比验证表明:该算法计算得到有效性评价系数平均值为0.039 3,比其他方法具有更强的抗噪性和适用性.
According to the situation that traditional methods of extracting crack image information have limitations,a method for extracting surface cracks of engineering structures based on multiple connected domain features was proposed.By using maximum inter-class variance(Otsu) method to split original image initially,K-means clustering algorithm was used to analyze the characteristic parameters of connected domain,including the area of connected domain,minimum length-width ratio of connected domain bounding box and the furthest distance in connected domain in crack image,and the distribution range of characteristic parameters in the connected region of crack target and noise background region were obtained.Then,a new method for extracting crack information was established,and the flow chart of the algorithm was given.The new method was verified by comparing with other methods,and the average validity evaluation coefficient calculated by this algorithm is 0.039 3,which is proved to have better noise resistance and applicability compared with other methods.
According to the situation that traditional methods of extracting crack image information have limitations,a method for extracting surface cracks of engineering structures based on multiple connected domain features was proposed.By using maximum inter-class variance(Otsu) method to split original image initially,K-means clustering algorithm was used to analyze the characteristic parameters of connected domain,including the area of connected domain,minimum length-width ratio of connected domain bounding box and the furthest distance in connected domain in crack image,and the distribution range of characteristic parameters in the connected region of crack target and noise background region were obtained.Then,a new method for extracting crack information was established,and the flow chart of the algorithm was given.The new method was verified by comparing with other methods,and the average validity evaluation coefficient calculated by this algorithm is 0.039 3,which is proved to have better noise resistance and applicability compared with other methods.
引文
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[3]孙文雅,黄民,李天剑,等.基于BP神经网络管道裂缝图像分割[J].计算机测量与控制,2012,20(5):214-215.
[4]刘瑞林,谢芳,肖承文,等.基于小波变换图像分割技术的电成像测井资料裂缝、孔洞面孔率提取方法[J].地球物理学报,2017,60(12):4945-4955.
[5]董安国,梁苗苗.基于灰度相关性的裂缝检测算法[J].计算机应用研究,2013,30(10):3121-3123.
[6]徐志刚,赵祥模,宋焕生,等.基于直方图估计和形状分析的沥青路面裂缝识别算法[J].仪器仪表学报,2010,31(10):2260-2266.
[7]王睿,漆泰岳,朱鑫,等.隧道检测裂缝的图像处理研究[J].铁道标准设计,2014(10):93-96.
[8]李淼,杨恢先,张建波,等.改进二维直方图区域划分的阈值分割方法研究[J].光电子·激光,2013(7):1426-1433.
[9]何志勇,孙立宁,黄伟国,等.基于Otsu准则和直线截距直方图的阈值分割[J].光学精密工程,2012,20(10):2315-2323.
[10]李锋,阚建霞.基于Sobel算子的图像快速二维最大熵阈值分割算法[J].计算机科学,2015,42(6):209-211.
[11]焦莉.面向对象方法的路面线性裂缝识别与测量方法研究[J].测绘通报,2012(7):27-30.
[12]何程涛,张仁杰.基于HALCON的电池盖帽检测[J].数据通信,2017(4):36-39.
[13] AKIYAMA T,MASUDA I.A method of documentimage segmentation based on projection profiles,stroke densities and circumscribed rectangles[J].Systems&Computers in Japan,2010,18(4):101-111.
[14] KALENTEV O,RAI A,KEMNITZ S,et al.Research note:connected component labeling on a 2D grid using CUDA[J].Journal of Parallel&Distributed Computing,2011,71(4):615-620.
[15]顾桂鹏,张钰婷,邵勇.规则工件几何尺寸图像测量研究[J].工业控制计算机,2017(2):99-100.
[16] HAN J,KAMBER M,PEI J.Data mining:concepts and techniques:concepts and techniques[J].Data Mining Concepts Models Methods&Algorithms,2011,5(4):1-18.
[17]黄韬,刘胜辉,谭艳娜.基于k-means聚类算法的研究[J].计算机技术与发展,2011,21(7):54-57.
[18] ZHANG L,CHEN Y,YE J I,et al.Research on K-means algorithm based on density[J].Researchof Computers,2011,28(11):4063-4071.
[19] WANG T Z.The development of web log mining based on improve-K-means clustering analysis[M].Berlin:Springer,2012.
[20] GUO W Y,WANG X F,XIA X Z.Two-dimensional Otsu's thresholding segmentation method based on grid box filter[J].Optik-International Journal for Light and Electron Optics,2014,125(18):5234-5240.
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