Abstract
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.
Highlights
In the fabric industry, fabric production is usually done on weaving and knitting machines.[1]
The 1st row displays original defective images; the 2nd row is corresponding ground-truth (GT) images; the 3rd row displays the results of the low-rank decomposition (LR) method; the 4th row exhibits the results of the weighted low-rank decomposition model (WLR) method; the 5th row shows the with Laplacian regularization (WLRL) detection results
LSG and WLRL detection results are significantly higher than other methods
Summary
Fabric production is usually done on weaving and knitting machines.[1] this process always produces various defects, such as oil stain and knotting. Fabric defects are one of the main factors affecting the quality of fabrics. Considering that the loss of fabric profits caused by defects can reach 45%: 65%,2 defect detection is a necessary step in fabric production. Defect detection mainly depends on experienced skilled workers, but this method is too subjective and inefficient. Automated visual inspection of fabrics can make up for these shortcomings.[3] the research on automatic fabric defect detection has far-reaching significance
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