Recognition method for woven fabric structure

Abstract

The invention relates to an automatic recognition method for a woven fabric structure based on gradient direction characteristics and Fuzzy C-Means Algorithm (FCM). The automatic recognition method comprises the following steps of: firstly preprocessing a woven fabric brightness image by adopting an image morphology method, then correcting deflection existing in interweaving of warp yarns and weft yarns by utilizing gray projection, simultaneously segmenting a fabric image into a plurality of weave points, extracting gradient direction histogram characteristics on each weave point, classifying the weave points by using an improved FCM, and finally carrying out statistics on classifying results according to the periodicity of the fabric structure and correcting error checking points, thus outputting a correct weave chart. The automatic recognition method can overcome the influence brought by uneven illumination, and difference in thickness and color of yarns by utilizing gradient direction information of the weave points and combining with the FCM method, can achieve recognition of basic weaves (a plain weave, a twill weave and a satin weave) of the woven fabric, and also has a good recognition effect on derivative weaves (a plain derivative weave, a twill derivative weave and a satin derivative weave) in small decorative pattern derivative weaves.

Description

technical field [0001] The invention relates to an automatic recognition method for the weave structure of a woven fabric. The method has a good recognition effect on the original weave of the woven fabric with changes in yarn thickness and color and the change in the small pattern weave, and belongs to the technical field of image processing. It can be applied to the automatic detection of woven cloth in the textile field. Background technique [0002] At present, in the textile industry, the structure of the woven fabric is generally identified manually by using a cloth mirror, or the enlarged weaving picture is first collected and then manually identified. This recognition method not only consumes a lot of manpower and time, but also different inspectors often give inconsistent detection results. In addition, it is difficult to manually recognize complex fabrics because human eyes are prone to fatigue. Therefore, the establishment of an automatic recognition system for t...

AI Technical Summary

Problems solved by technology

First of all, the gray projection curve along the horizontal and vertical directions can be used to accurately segment the weave points of woven fabrics. However, in practice, the warp and weft yarns of some woven fabrics are not vertically interwoven, and there is a small skew, which has a great impact on the segmentation. , it is necessary to judge its deflection angle first and then perform projection segm...

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