The purpose of this paper is to focus on the design of automated fabric defect detection based on cascaded low-rank decomposition and to maintain high quality control in textile manufacturing.,This paper proposed a fabric defect detection algorithm based on cascaded low-rank decomposition. First, the constructed Gabor feature matrix is divided into a low-rank matrix and sparse matrix using low-rank decomposition technique, and the sparse matrix is used as priori matrix where higher values indicate a higher probability of abnormality. Second, we conducted the second low-rank decomposition for the constructed texton feature matrix under the guidance of the priori matrix. Finally, an improved adaptive threshold segmentation algorithm was adopted to segment the saliency map generated by the final sparse matrix to locate the defect regions.,The proposed method was evaluated on the public fabric image databases. By comparing with the ground-truth, the average detection rate of 98.26% was obtained and is superior to the state-of-the-art.,The cascaded low-rank decomposition was first proposed and applied into the fabric defect detection. The quantitative value shows the effectiveness of the detection method. Hence, the proposed method can be used for accurate defect detection and automated analysis system.

中文翻译：

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基于级联低秩分解的织物疵点检测方法

本文的目的是着眼于基于级联低秩分解的自动织物疵点检测的设计，并在纺织品制造中保持高质量控制。本文提出了一种基于级联低秩分解的织物疵点检测算法。首先，使用低秩分解技术将构建的 Gabor 特征矩阵划分为低秩矩阵和稀疏矩阵，并将稀疏矩阵用作先验矩阵，其中较高的值表示较高的异常概率。其次，我们在先验矩阵的指导下对构建的文本特征矩阵进行第二次低秩分解。最后，采用改进的自适应阈值分割算法对最终稀疏矩阵生成的显着图进行分割，定位缺陷区域。所提出的方法在公共织物图像数据库上进行了评估。通过与ground-truth对比,平均检出率为98.26%,优于现有技术。首先提出级联低秩分解,并将其应用于织物疵点检测。定量值表明了检测方法的有效性。因此，所提出的方法可用于准确的缺陷检测和自动分析系统。