This paper introduces a related model of membrane calculation in the defect detection and positioning of industrial components. It has the characteristics of distributed and parallel computing, and can efficiently search for better solutions in a given feature space. Inspired by the membrane clustering algorithm, this paper proposes a greedy membrane clustering algorithm and names it GMCA. GMCA is applied after the extraction of local features of normal samples. It uses a greedy strategy to construct a sub-feature set that describes the local characteristics of normal samples. During training, GMCA can learn the membrane cluster center of normal image blocks and each sub-feature within the cluster. At test time, the anomaly map is obtained by calculating the distance from the test sample block to the corresponding cluster center and the maximum distance from the cluster center to the nearest neighbor in the training sample. This solves the limitation of traditional algorithms requiring dataset alignment. In the unsupervised dataset MvTec AD, samples can be divided into object categories and texture categories according to the background of images. The pixel-level anomaly location index (AUROC) of this method on object category data reaches 98.3%. The image-level anomaly detection index (AUROC) on texture category data reaches 99.1%.

中文翻译：

---

基于贪婪膜聚类算法的工业缺陷检测与定位

本文介绍了工业部件缺陷检测与定位中薄膜计算的相关模型。它具有分布式、并行计算的特点，能够在给定的特征空间中高效地搜索更好的解。受膜聚类算法的启发，本文提出了一种贪婪膜聚类算法，并将其命名为GMCA。 GMCA是在提取正常样本的局部特征后应用的。它采用贪心策略构造描述正常样本局部特征的子特征集。在训练过程中，GMCA可以学习正常图像块的膜簇中心以及簇内的每个子特征。在测试时，通过计算测试样本块到相应聚类中心的距离以及聚类中心到训练样本中最近邻的最大距离来获得异常图。这解决了传统算法需要数据集对齐的限制。在无监督数据集MvTec AD中，样本可以根据图像的背景分为对象类别和纹理类别。该方法在物体类别数据上的像素级异常位置指数（AUROC）达到98.3%。纹理类别数据的图像级异常检测指数（AUROC）达到99.1%。