刹车片表面缺陷的图像检测方法

针对汽车刹车片表面纹理情况不一、种类繁多的问题，提出一种基于机器视觉的刹车片表面缺陷图像检测方法，该方法结合了灰度共生矩阵与密度聚类。首先提取刹车片的摩擦面，然后确定灰度共生矩阵的构造因子的选取，利用线性相关性选择特征参数。将每个摩擦面切分成若干小窗口，计算各个小窗口的特征值构造特征数据集，通过自适应密度聚类算法进行聚类分析，从而进一步判断是否存在缺陷区域。通过对58块不同型号的刹车片样本进行检测结果统计分析，实验表明，该方法误检率为8%，漏检率为6%，具有较高的检测精度，因此，该方法能够较好地检测刹车片样本是否存在缺陷，具有广泛的适用性。

Image detection method for surface defects of brake pads

In order to deal with the different surface textures and various types of automotive brake pads, an image detection method for surface defects of brake pads based on machine vision was proposed, which combined the gray co-occurrence matrix and the density clustering. Firstly, the friction surface of the brake pad was extracted. Then the construction factor selection of the gray co-occurrence matrix was determined, and the linear correlation was used to select the characteristic parameters. Each friction surface was divided into several small windows, and the eigenvalue of each small window was calculated to construct the feature data sets. The cluster analysis was carried out by the adaptive density clustering algorithm, so as to further determine whether there existed a defect area or not. Based on the statistical analysis of the test results of 58 brake pad samples of different models, the experiment shows that the method has a high detection accuracy with a false detection rate of 8% and a missed detection rate of 6%. Therefore, the method can better detect whether there are defects in the brake pad samples and has a wide range of applicability.