Fabric defect detection method based on distance matching function and perceptual hash algorithm

Abstract

The present invention discloses a fabric defect detection method based on a distance matching function and a perceptual hash algorithm. According to the method, the minimum period of a regular fabricis calculated through using the distance matching function, and the minimum period can be applied to obtaining image blocks; a repeat unit template is constructed, and the structure features of the template image block are extracted through adopting the perceptual hash algorithm; the structural features and grayscale features of image blocks of a sample to be tested are extracted, and are comparedwith the structural features and global gray average value of the template image block, so that a minimum Hamming distance feature image and a gray average value comparison feature image are obtained; and the merging and segmentation of the feature images are performed. The results show that the method of the present invention comprehensively considers the structural features and gray average value feature of the minimum period of the regular fabric, can effectively extract the defect area of the fabric and realize the defect detection of the fabric.

Description

technical field [0001] The invention belongs to the technical field of textile image processing, and in particular relates to a fabric defect matching detection method. Background technique [0002] Fabrics are the basis of everyday consumer products such as clothing, bags, bedding, medical fabrics and more. Fabric inspection is a critical part of quality control in textile production. Currently, most fabric inspections are performed by manual visual inspection which is costly, but unreliable due to human error and eye strain. The automatic visual inspection (AVI) of fabrics applies computer vision technology, which not only provides an efficient, low-cost and accurate method to replace labor, but also expands the inspection capacity to cover a wider range of different fabric patterns, from the simplest The most complex fabric patterns are available. The goal of AVI is to detect and delineate the shape and location of any defects on the fabric surface during or after fabr...

AI Technical Summary

Problems solved by technology

However, for patterned fabrics, the different textures and patterns of fabrics and the similarity between defects and backgrounds will bring great challenges to detection.

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