Fabric defect detection method based on sparse representation coefficient optimization

Abstract

The invention discloses a fabric defect detection method based on sparse representation coefficient optimization. The detection method comprises self-adaptive dictionary database study, sparse coefficient matrix optimization and image reconstruction as well as generation and segmentation of a vision saliency map and specifically comprises steps as follows: an image is partitioned into blocks, self-adaptive dictionary database study is performed, and a dictionary database is obtained; a sparse representation coefficient matrix is solved with an L2-norm minimization method, and abnormal coefficient elements in the obtained matrix are optimized; a fabric image is reconstructed with adoption of the obtained dictionary database and the optimized sparse representation coefficient matrix, the fabric image and a to-be-detected image are subjected to residual error processing, and a residual error saliency map is obtained; the saliency map is segmented with a maximum entropy threshold segmentation method, and a fabric defect detection result is obtained. Randomness of fabric textural features and diversity of defect varieties are overall considered, the to-be-detected fabric image is taken as a detection reference for a dictionary database studying sample and a defect area, the method has higher detection accuracy, no defect information is required to be extracted, and the self-adaptive capability is high; the computation speed is higher, and the method is suitable for online detection.

Description

technical field [0001] The invention belongs to the technical field of textile image processing, and in particular relates to a method for detecting and locating defects on fabric defect images by using an image sparse representation method and a saliency analysis method. Background technique [0002] Fabric defect detection is a key link in textile quality control and management. With the rapid development of integrated circuits and image processing technology, machine vision has been more and more widely used in the field of industrial surface inspection. Replacing manual operations with computer vision can not only increase the detection speed and reduce labor costs, but also through cloth The defect automatic detection system can provide credible reference standards for both parties for the assessment of cloth quality grades, which is beneficial to international trade. Fabric defect detection and discrimination algorithm is the core link of this kind of system, which di...

AI Technical Summary

Problems solved by technology

This method can reconstruct texture images of different fabrics, and has high self-adaptive characteristics; however, when this technology is applied to pattern fabric dictionary detection, the trained dictionary library is difficult to accurately represent normal textures, and the original fabric reconstruction error is relatively large. Large, resulting in false detection, missed detection, etc.

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