Textile defect detection method based on low-rank sparse matrix decomposition

Abstract

The invention relates to the technical field of textile detection, and particularly to a textile defect detection method based on low-rank sparse matrix factorization. The method comprises the following steps: firstly, partitioning a periodic textile according to periodic information to obtain defect priori for guiding a low-rank factorization model, and then adding Laplace regularization to increase the distance between a background and a defect area; and finally, segmenting the generated sparse matrix by adopting an optimal threshold segmentation algorithm to complete defect detection. The invention provides a textile detection method based on low-rank sparse matrix factorization. Defect prior is determined through a block segmentation method, the defect prior is added into a low-rank decomposition model, the weighted low-rank decomposition model is constructed, the detection precision of large defect blocks is improved, the distance between a defect area and a background is increased through a Laplace regularization term, and the detection precision and robustness of defects are further improved.

Description

technical field [0001] The invention relates to the technical field of textile detection, in particular to a textile defect detection method based on low-rank sparse matrix decomposition. Background technique [0002] P Textiles always have various defects in their production process, and textile defects are one of the main factors affecting the quality of textiles. Therefore, defect detection is an indispensable step in the textile production process. However, due to the complex and changeable texture of the textile image itself and various types of flaws, it brings certain challenges to the research of flaw detection algorithms. [0003] At present, textiles can be mainly divided into two categories: the first category is solid-colored textiles (such as plain and twill) with simple structures and no complex patterns; the other category is textile images with periodically changing patterns, one of which is called a block or pattern. A cycle, different types of fabrics (su...

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Problems solved by technology

[0007] The technical problem to be solved by the present invention is: in order to solve the problem that some large uniform defect blocks cannot be detected in the prior art, and when the background of the textile and the defect area are relatively consistent or the texture of the fabric image is complex, it is difficult to separate them by previous methods out of the problem, a textile detection method based on low-rank sparse matrix decomposition is proposed, which determines the flaw prior through the block segmentation method, adds it to the low-rank decomposition model, and constructs a low-rank decomposition model of weight, which increases the weight of the larger The detection accuracy of the defect block, and through the Laplacian regular term, the distance between the defect area and the background is increased to improve the detection accuracy and robustness of the defect

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