Jie Zhang

Automatic recognition of the color effect of yarn-dyed fabric by the smallest repeat unit recognition algorithm

In this paper, an effective method based on the smallest repeat unit recognition (SRUR) algorithm is proposed to inspect the color effect of yarn-dyed fabric automatically. This method consists of three main steps: (1) color pattern preliminary recognition; (2) weave repeat unit recognition; (3) color yarn repeat unit recognition. In the first step, the floats in the fabric are located by yarn position segmented with mathematical statistics of sub-images and the colors of all floats classified by the fuzzy C-means algorithm. The color yarn layout is recognized by statistical analysis and the color pattern is roughly generated. In the second step, the weave repeat unit is found based on the preliminary color pattern. The weave repeat unit is extracted from the incompletely recognized weave pattern matrix by the SRUR algorithm. In the last step, according to the weave repeat unit and the preliminary identified color pattern, the color yarn layout is rectified by the improved statistical analysis, and the color yarn repeat unit is finally obtained by the SRUR algorithm. According to the weave and color yarn repeat units, the color effect is produced. The experimental analysis proved that the proposed method can recognize color effects of yarn-dyed fabrics with satisfactory accuracy.

Automatic inspection of yarn-dyed fabric density by mathematical statistics of sub-images

To inspect the yarn-dyed fabric density automatically, an effective image analysis method based on mathematical statistics of sub-images is proposed in this paper. This method consists of two main steps: rough measurement and precise measurement. The rough measurement is based on projection curve of the whole fabric image. The fabric image is converted into HSV model from RGB model firstly, and then the projection curve of value is gained directly. The number of yarns is obtained by counting the number of peaks in the curve roughly. The precise measurement is based on projection curves of the fabric sub-images. According to the roughly estimated yarn number, the whole fabric image is divided into a certain amount of sub-images and the projection method is applied to all the sub-images, respectively. The probability distribution map of peaks is obtained by processing the projection curves of all sub-images and the positions of the yarn center are located in the frequency curve generated from the map by mathematical statistics method. The number of peaks in the frequency curve is counted, and, therefore, the number of yarns is detected, and the density can be calculated precisely. The experimental results proved that the proposed method is effective for yarn-dyed fabrics and can satisfy the requirement for production practice.

Yarn-Dyed Fabric Defect Detection Based On Autocorrelation Function And GLCM

Abstract In this study, a new detection algorithm for yarn-dyed fabric defect based on autocorrelation function and grey level co-occurrence matrix (GLCM) is put forward. First, autocorrelation function is used to determine the pattern period of yarn-dyed fabric and according to this, the size of detection window can be obtained. Second, GLCMs are calculated with the specified parameters to characterise the original image. Third, Euclidean distances of GLCMs between being detected images and template image, which is selected from the defect-free fabric, are computed and then the threshold value is given to realise the defect detection. Experimental results show that the algorithm proposed in this study can achieve accurate detection of common defects of yarn-dyed fabric, such as the wrong weft, weft crackiness, stretched warp, oil stain and holes.

Measuring the unevenness of yarn apparent diameter from yarn sequence images

This article presents a novel method for measuring the unevenness of yarn apparent diameter based on yarn sequence images captured from a moving yarn. A dynamic threshold module was designed to gain the global threshold for segmenting yarns in the sequence images. In the module, a K-means clustering algorithm was employed to classify the pixels of each frame in the sequence into two clusters—yarn and background. The cluster center of the current frame was used as the initial value of the cluster center for the next frame in the sequence to expedite the segmentation process. From the segmented yarn image, the yarn core was further extracted utilizing the characteristics of yarn hairiness, and two judgment templates were adopted to remove burrs, isolated points and unrelated small areas in the images. The yarn apparent diameter was measured on the yarn core at a given interval. The same kind of yarns were tested by using this method and Uster Evenness Tester 5. The experimental results show that the proposed method can accurately detect the unevenness of yarn apparent diameter and provide new useful information about yarn unevenness, such as the short-term, the long-term, and the periodic variations of yarn apparent diameters.

Applying Image Analysis for Automatic Density Measurement of High-tightness Woven Fabrics

To realise the density measurement of high-tightness woven fabrics, an efficient inspection method based on the structure relation is developed in this paper. The structure relations of typical HTWF, twill and satin weave are analyzed and a calculation equation of warp density is given with the fabric weave, weft density and wale density. In the experiment, the weft and wale densities are measured with the Fourier transform, image reconstruction and threshold processing based on separately captured images. The warp density is finally calculated based on the mean value of wale and weft density and the given calculation equation constructed with the weave pattern. The experimental results prove that the automatic measurement density system can realize the precise measurement of high-tightness woven fabric density with satisfactory precision and can replace the current manual analysis method.

Automatic inspection of density in yarn-dyed fabrics by utilizing fabric light transmittance and Fourier analysis.

Yarn density measurement is a significant part of yarn-dyed fabric analysis, traditionally based on reflective image analysis. In this paper, utilizing fabric light transmittance, a method for two-dimensional discrete Fourier transform (2D DFT) analysis on the transmission fabric image is developed for fabric density inspection. First, the power spectrum is generated from the fabric image by a 2D DFT. Next, the yarn skew angles are detected based on the power spectrum analysis. Then the fabric image is reconstructed by an inverse 2D DFT. Finally, projection curves are generated from the reconstructed images and the number of yarns is counted according to the peaks and valleys to obtain the fabric density. Through a comparison between analysis on the reflective and transmission images of multiple-color fabrics, it is proved that the latter method can segment the yarns with more satisfactory accuracy. Furthermore, the experimental and theoretical analyses demonstrate that the proposed method is effective for the density inspection of yarn-dyed fabrics with good robustness and great accuracy.

An efficient method for density measurement for high-tightness woven fabrics

To measure the warp and weft densities of high-tightness fabrics, an efficient inspection method utilizing a novel density–texture formulation is developed. The density–texture relationship of twill and satin weaves is analyzed, and a formulation based on the weft and wale densities is developed and applied to calculate the warp density. In the experiment, the weft and wale densities are detected using a projection method on separately acquired transmittance images, and the warp density is calculated using the density–texture formulation. Experimental results proved that the proposed method is effective for measuring yarn densities of high-tightness fabrics with twill and satin weaves under realistic production conditions and can satisfy the requirement for production practice.

Weave pattern recognition by measuring fiber orientation with Fourier transform

Abstract An effective method based on measuring the fiber orientation of yarn floats with two-dimensional Fourier transform (2-D FFT) is proposed to recognize the weave pattern of yarn-dyed fabric in the high-resolution image. The recognition process consists of four main steps: 1. High-resolution image reduction, 2.Fabric image skew correction, 3.Yarn floats localization, 4. Yarn floats classification. Firstly, the high-resolution image is reduced by the nearest interpolation algorithm. Secondly, the skew of the fabric image is corrected based on Hough transform. Thirdly, the yarn floats in the fabric image is localized by the yarns segmentation method based on the mathematical statistics of sub-images. Fourthly, the high-resolution image is corrected and its yarns are segmented successively according to the inspection information of the reduced image. The fiber orientations are detected by 2-D FFT, and the yarn floats are classified by k-means clustering algorithm. Experimental results and discussions demonstrate that, by measuring the fiber orientation of yarn floats, the proposed method is effective to recognize the yarn floats and the weave pattern for yarn-dyed, solid color, and gray fabrics.