Fabric defect detection method based on multi-feature matrix low-rank decomposition

Abstract

The invention discloses a fabric defect detection method based on multi-feature matrix low-rank decomposition. The method comprises the steps of image blocking, multi-channel feature matrix extraction, united low-rank decomposition and saliency map generation and partitioning, wherein a fabric image is divided into image blocks with the same size, a second-order gradient direction map of each image block is calculated, a retina P-type ganglion cell coding mode is adopted to extract image features, and a feature matrix is generated; an effective low-rank decomposition model is constructed according to the feature matrix, optimal solving is performed through a direction alternating multiplier method, and a low-rank matrix and a sparse matrix are generated; and a threshold segmentation algorithm is adopted to partition a saliency map generated by the sparse matrix, and defect positions are found. According to the method, the complexity of fabric texture features and the diversity of defect types are comprehensively considered, second-order features capable of effectively representing the fabric texture features are extracted, the untied low-rank decomposition model is adopted to effectively realize quick separation of defects and a background, and the method has high detection precision.

Description

technical field [0001] The invention relates to the technical field of textile image processing, in particular to a fabric defect detection method based on multi-feature matrix low-rank decomposition, using multi-channel second-order gradient feature extraction and low-rank decomposition methods to detect and locate fabric defect images . Background technique [0002] Fabric defect detection is an important part of textile quality control. The results of traditional manual inspection are greatly influenced by human subjectivity, which makes it difficult to guarantee the accuracy and real-time performance of inspection. Therefore, the automatic detection technology of fabric defects based on image processing has become a research hotspot in recent years. [0003] At present, according to different types of fabrics, defect detection algorithms are mainly divided into two categories, one is for plain or twill images with a relatively simple background, and the other is for pa...

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Problems solved by technology

[0008] Aiming at the technical problems that the existing defect detection methods cannot adapt to more types of cloth and fabric defects, and the detection accuracy is low, the present invention proposes a fabric defect detection method based on multi-feature matrix low-rank decomposition, and proposes a multi-channel two-step High-degree feature extraction method to generate multiple feature matrices; use the joint low-rank decomposition method to decompose the multi-channel feature matrix to obtain low-rank matrix and sparse matrix; generate a saliency map from the sparse matrix, locate the defect area after segmentation, and realize Effective detection and positioning of fabric image defects with high detection accuracy

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