基于计算机视觉的混凝土坝裂缝检测方法

Crack detection method of concrete dams based on computer vision

为解决传统图像分割算法难以分割噪声污染严重的坝面裂缝图片的问题,提出一种基于自适应区域生长和局部K-Means聚类的坝面裂缝检测算法。采用双边滤波对裂缝灰度图进行初步降噪,运用自适应区域生长算法获得裂缝的粗分割图,再通过形态学腐蚀操作以及最大连通域提取操作去除孤立的点状及团状噪声,最后采用局部K-means聚类算法获得裂缝的精细分割图。运用该算法以及大津阈值分割算法等常用的3种算法对3幅分别存在污渍噪声、混凝土表面毛刺噪声、块状以及条状噪声污染的裂缝图片进行分割。结果表明:该算法获得的分割结果完成度指数以及正确度指数均在0.95以上,高于其他3种算法;该算法的抗噪性好,适应性强,能实现对坝面裂缝的精确识别分割。

In order to solve the problem that the traditional image segmentation algorithm is difficult to segment the dam surface crack images with serious noise pollution,a dam surface crack recognition and segmentation algorithm based on adaptive region growing and local K-Means clustering was proposed.Firstly,bilateral filtering to initially reduce noise in crack greyscale images is used,then the adaptive region growing algorithm to obtain the rough segmentation image of cracks is applied.Secondly,the isolated noises like points and groups are removed by morphological eroding and largest connected domain extracting.Finally,the precise segmentation image of cracks is obtained by local K-Means clustering algorithm.The proposed algorithm,Otsu threshold algorithm and other three algorithms are used to segment three crack images which have stain noise,concrete surface burr noise,block noise and strip noise respectively.The results show that the completion index and accuracy index of the segmentation results obtained by the proposed algorithm are above 0.95,which is better than the other three algorithms.The proposed algorithm has good anti-noise performance and strong adaptability,which can realize the accurate identification and segmentation of dam surface cracks.