Low-rank decomposition models have potential for fabric defect detection, where a feature matrix is decomposed into a low-rank matrix that corresponding to repeated texture structure and a sparse matrix that represent defective regions. Two limitations, however, still exist. First, previous work might fail to detect some large homogeneous defective block. Second, when the background and defective regions are relatively coherent or the texture of fabric image is complex, it is difficult to use previous methods to separate them. To deal with these problems, a new weighted low-rank decomposition model with Laplace regularization (WLRL) is proposed in this paper: (1) a weighted low-rank decomposition model that can decompose the original image into background and defective regions, and (2) a Laplace regularization that can enlarge the distance between the background and the defective regions. The performance of the proposed method WLRL is evaluated on the box- and star-patterned fabric databases, and superior results are shown compared with state-of-the-art methods, that is, 98.70% ACC (accuracy) and 72.83% TPR (true positive rate) for box-patterned fabrics, 99.09% ACC (accuracy) and 83.63% TPR (true positive rate) for star-patterned fabrics.

中文翻译：

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通过加权低秩分解和拉普拉斯正则化检测织物缺陷

低秩分解模型具有用于织物缺陷检测的潜力，其中特征矩阵被分解为对应于重复纹理结构的低秩矩阵和代表缺陷区域的稀疏矩阵。但是，仍然存在两个限制。首先，以前的工作可能无法检测到一些大的同质缺陷块。其次，当背景和缺陷区域相对连贯或织物图像纹理复杂时，很难使用以前的方法将它们分开。针对这些问题，本文提出了一种新的带拉普拉斯正则化（WLRL）的加权低秩分解模型：（1）一种可以将原始图像分解为背景和缺陷区域的加权低秩分解模型，(2) 拉普拉斯正则化，可以扩大背景和缺陷区域之间的距离。所提出的方法 WLRL 的性能在盒形和星形图案的织物数据库上进行了评估，与最先进的方法相比，显示出优越的结果，即 98.70% ACC（准确度）和 72.83% TPR（真阳性率）对于箱形图案面料，99.09% ACC（准确度）和 83.63% TPR（真阳性率）对于星形图案面料。