Vision conspicuousness-based cloth flaw detection method

Abstract

The invention discloses a vision conspicuousness-based cloth flaw detection method which comprises the following steps: (1) collecting an image; (2) processing brightness features; (3) processing direction features; (4) performing multichannel superposition normalizing processing; (5) processing a grey-scale map; (6) performing binaryzation processing; (7) judging a flaw area. Compared with the traditional cloth flaw detection method, the vision conspicuousness-based cloth flaw detection method has the advantages that the operation complexity is reduced, the recognition rate is increased, accurate positioning can be realized, false detection easily caused under the condition that a gray value of a conspicuousness image of a perfection image of detected cloth is higher than a gray value of a perfection part in a flaw image is avoided, the interference of a background during detection is effectively reduced, and the condition that a target area obtained by performing adaptive threshold segmentation on an image of perfect cloth is mistakenly judged to be the flaw area is reduced.

Description

technical field [0001] The invention relates to a cloth defect detection method, in particular to a cloth defect detection method based on visual salience. Background technique [0002] In modern textile production, quality control and testing are very important, and cloth defect detection is a particularly critical component. At present, domestic textile enterprises mainly use manual testing methods, and the detection speed of human eyes is limited, and the detection results are easily affected. Influenced by subjective factors, false detection and missed detection are prone to occur. Replacing manual cloth defect detection with advanced automatic detection technology is an important measure to improve detection efficiency, reduce labor, reduce labor intensity and ensure cloth quality. Scholars at home and abroad have made many outstanding achievements in the research of automatic detection methods. [0003] The cloth defect detection algorithm mainly judges the defect ac...

AI Technical Summary

Problems solved by technology

The Fourier transform is a global transformation of the image, so it cannot accurately locate the defect; the transformation detection performance of the Gabor analysis is better, but it needs to perform two-dimensional filtering and fusion on the multi-channel direction, which greatly increases the computational complexity; the wavelet transform has a good Local time-frequency analysis, fast calculation speed, etc., but the poor direction selectivity makes it unable to describe the characteristics of the two-dimensional graph well, resulting in unsatisfactory detection results

Images