隧道裂缝图像智能匹配与变化趋势检测算法研究

发布时间：2018-04-12 21:00

本文选题：裂缝检测 + 图像处理　；参考：《北京交通大学》2017年硕士论文

【摘要】：裂缝是影响隧道内部安全的重要因素之一,并且随着时间推移可能发生进一步形变,最后对地铁运营安全造成严重影响,所以要对特定的危险裂缝进行密切监测。近几年,视觉检测与图像处理技术,成为隧道裂缝病害自动检测的主要方法。进行裂缝检测时,由于外部环境及拍摄设备的原因,不同时间拍摄得到的同一裂缝所在图像,会出现裂缝产生一定的平移、旋转甚至缩放等问题。本文提出了一种基于图像处理的地铁隧道裂缝匹配和变化趋势检测算法,能够进行裂缝图像匹配,在大量图像库中检测出包含指定裂缝的图像,并比较裂缝宽度变化。本文首先利用MASK算法对隧道裂缝图像进行匀光处理,平衡图像光照,增强对比度,然后采用OTSU方法对裂缝图像进行阈值分割,接下来采用基于连通区域的两步滤波算法滤除噪声,得到只包含裂缝信息的二值图像。针对此二值图像,采用细化算法和去分支算法进行处理,得到裂缝骨架图。为了提取图像的特征,便于下一步匹配,本文设计了裂缝图像特征提取算法,采用改进的Harris算子提取裂缝骨架图的特征点,并设计算法对特征点矩阵进行扩充和归一化,得到最终的特征点集。接下来要进行裂缝图像特征点集匹配,本文提出了基于Frechet距离和Hausdorff距离的点集配准算法进行裂缝特征点匹配。在裂缝变化趋势检测时,本文采用将二值图像与灰度图相结合的方法计算裂缝宽度,取1-σ准则作为裂缝宽度计算准则,在像素域进行宽度计算。采用MATLAB仿真的方法使得裂缝宽度发生变化,并将变化前后的宽度计算结果进行比较,验证了该宽度计算方法的准确性。最后采用本文提出的裂缝图像匹配算法,对墙面、路面、隧道三种场景中采集的裂缝图像进行实验,实验表明文中的裂缝图像匹配算法在三种场景下都表现良好。在路面裂缝图像和地铁隧道裂缝图像匹配实验中,算法误检率分别为8%和19%。
[Abstract]:Crack is one of the important factors that affect the safety of tunnel, and with the passage of time, further deformation may take place, and finally the safety of subway operation will be seriously affected. Therefore, it is necessary to monitor the specific dangerous cracks closely.In recent years, vision detection and image processing technology have become the main methods for automatic detection of tunnel cracks.In the process of crack detection, due to the external environment and shooting equipment, some problems such as translation, rotation and even scaling will occur in the image of the same crack taken at different times.In this paper, an algorithm of crack matching and trend detection for subway tunnel based on image processing is proposed, which can match crack images, detect images containing specified cracks in a large number of image databases, and compare the variation of crack width.In this paper, we first use MASK algorithm to smooth the tunnel crack image, balance the illumination of the image, enhance the contrast, and then use the OTSU method to segment the crack image by threshold.Then, a two-step filtering algorithm based on connected region is used to filter the noise, and the binary image containing only crack information is obtained.For this binary image, thinning algorithm and debranching algorithm are used to process the crack skeleton.In order to extract the feature of the image and facilitate the next step matching, this paper designs a feature extraction algorithm for the crack image. The improved Harris operator is used to extract the feature points of the crack skeleton graph, and the algorithm is designed to expand and normalize the feature point matrix.The final feature point set is obtained.Next, the feature point set matching of fracture image is carried out. In this paper, the point set registration algorithm based on Frechet distance and Hausdorff distance is proposed for crack feature point matching.In this paper, the method of combining binary image with gray image is used to calculate the crack width, and 1- 蟽 criterion is taken as the crack width calculation criterion, and the width is calculated in pixel domain.The MATLAB simulation method is used to make the crack width change, and the results of width calculation before and after the change are compared to verify the accuracy of the method.Finally, the crack image matching algorithm proposed in this paper is used to carry out experiments on the crack images collected in three kinds of scenes: metope, road surface and tunnel. The experiments show that the crack image matching algorithm in this paper performs well in the three kinds of scenes.In the matching experiment of pavement crack image and subway tunnel crack image, the false detection rate of the algorithm is 8% and 19% respectively.
【学位授予单位】：北京交通大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：U457;TP391.41

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