基于图像分析的桥梁裂缝检测方法研究

发布时间：2018-05-09 03:17

本文选题：爬壁机器人 + 运动模糊　；参考：《中国科学技术大学》2016年硕士论文

【摘要】：在我国交通运输建设过程中,公路桥梁是重要的组成部分。现阶段,我国每年步入维修期的公路桥梁数目在逐步增加,对桥梁安全检测特别是桥梁裂缝检测的需求已经上升到了一个新的高度。传统的人工检测方法不仅工作危险性大、作业成本高,而且工作效率低、检测结果可靠性低。因此,有必要提出新的可视化裂缝检测方法完成桥梁健康评估。本文工作基于一款自主研发的可实时获取桥梁壁面图像的仿生足式攀爬机器人,提出了用于桥梁结构裂缝检测的图像处理算法,并运用基于机器学习的方法完成对裂缝的分类。具体包含了以下几个方面工作：1)图像去运动模糊。实际工作过程中,爬壁机器人与桥梁裂缝之间有相对运动,摄像头拍摄到的裂缝图片含有运动模糊。本文在实验比较了维纳滤波、约束最小二乘方滤波、逆滤波这三种不同图像复原方法后,使用维纳滤波对裂缝图片进行复原。2)裂缝图像检测。本文提出了一种提取图片中裂缝目标的图像处理算法,首先对复原后的裂缝图片进行小波粗分割,然后用图像形态学分析方法去除图像中噪声块,初步分割出裂缝目标。3)裂缝连接。上述裂缝检测方法会破坏裂缝连接性,为了使得裂缝更加完整、连续,本文提出了一种基于KD树思想的裂缝连接方法将断开的裂缝连接起来。首先计算每段裂缝的最小外接凸多边形找出裂缝端点；然后判断不同裂缝段端点之间的像素距离是否小于设定的阈值,如果小于则连接两个端点；最后根据灰度特征将连线之间的裂缝连通域进行填充,分割出完整裂缝。结果表明,本文提出的裂缝连接方法效果显著。4)裂缝分类。不同类型的裂缝对桥梁的危害不同,因此在分割出完整裂缝目标后,有必要对裂缝进行分类。本文提出了一种基于支持向量机决策树的学习型方法用于裂缝分类。首先提取6类裂缝特征；其次根据训练样本训练出所需SVM决策树分类器；最后将训练好的分类器对验证样本集进行分类。结果表明,本文提出的分类方法分类精度较高。目前,使用数字图像处理技术对混凝土结构桥梁的裂缝进行检测的研究得到了广泛的关注和支持。本文研究的可视化检测诊断方法不仅可以大大解放劳动力,降低检测成本和维护成本,而且也能够有效排除人为因素的主观干扰,具有很高的实用价值和应用前景。
[Abstract]:In the process of transportation construction in China, highway bridge is an important part. At present, the number of highway bridges entering the maintenance period is increasing step by step, and the demand for bridge safety detection, especially bridge crack detection, has risen to a new height. The traditional manual detection method not only has high working risk, high activity cost, but also low work efficiency and low reliability of test results. Therefore, it is necessary to propose a new visual crack detection method to complete bridge health assessment. This paper presents an image processing algorithm for bridge structure crack detection based on a self-developed bionic foot climbing robot which can obtain the bridge wall image in real time. Specifically includes the following aspects of work: 1) Image deblurring. In the actual working process, there is a relative motion between the climbing robot and the cracks of the bridge, and the images of the cracks taken by the camera contain motion blur. In this paper, three different image restoration methods, Wiener filter, constrained least square filter and inverse filter, are compared experimentally. In this paper, an image processing algorithm is proposed to extract the crack target in the image. Firstly, the reconstructed crack image is segmented by wavelet transform, and then the noise block is removed by morphological analysis. Fracture target. 3) fracture connection. In order to make the fracture more complete and continuous, this paper proposes a method based on KD tree to connect the broken cracks. At first, the minimum convex polygon of each crack is calculated to find out the crack end point, and then the pixel distance between different crack segments is determined whether the pixel distance is less than the set threshold, and if it is smaller, two endpoints are connected. Finally, according to the grayscale feature, the crack connected region between the lines is filled, and the complete crack is segmented. The results show that the effect of the proposed method is significant. 4) fracture classification. The damage of different types of cracks to bridges is different, so it is necessary to classify the cracks after the complete crack targets are separated. This paper presents a learning method based on support vector machine decision tree for crack classification. First, six types of crack features are extracted; secondly, the required SVM decision tree classifier is trained according to the training sample; finally, the trained classifier is used to classify the test sample set. The results show that the classification method proposed in this paper has higher classification accuracy. At present, the application of digital image processing technology to the detection of cracks in concrete structure bridges has received extensive attention and support. The visual detection and diagnosis method studied in this paper can not only greatly liberate the labor force, reduce the cost of detection and maintenance, but also effectively eliminate the subjective interference of human factors. It has high practical value and application prospect.
【学位授予单位】：中国科学技术大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP391.41

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