Evaluation of yarn evenness in fabric based on image processing

Abstract

The traditional approach to unevenness characterization of yarn is based on the CV (i.e. coefficient of variation) of mass between defined portions of yarn measured with the USTER evenness tester. In fact, yarn with the same parameters has different evenness in fabric, and the evenness of yarn in fabric is different from the source yarn, which is caused by the producing process and parameters of the fabric. However, there is no good method to describe the appearance of yarn in fabric. The paper focuses mainly on a novel method known as yarn evenness in fabric (YEF). The method processes the image of the fabric and is divided into four steps. The first step is the acquisition of the relative image from the sample of woven fabric. The second step is the pretreatment of the image and segmentation of the warp and weft from the fabric based on fast Fourier transform and inverse fast Fourier transform. The third step is to separate the single yarn from the warp or weft sets by the gray unidirectional mean method. In the fourth step, the average relative thickness was selected for characterizing yarn. SD data of thickness that we will analyze in future is also a method. Experimental results on virtual and physical woven fabric showed that the method mentioned can obtain the fine information of yarn from fabric in details. The method of YEF was programmed by Matlab software. Computational burdens are about 11.4 seconds on average, for one meter of warp and weft yarn samples. The program could be valuable when applied to the practical industry.

Automatic Construction of Digital Woven Fabric by Using Sequential Yarn Images

“…In general, this CV value is seen as the main parameter to determine the quality of fabric. In fact, yarn with the same CV value may have different evenness in fabric [1]. This is due to the reality that the traditional method of detecting the CV value essentially measures the fiber content in the yarn rather than the yarn surface appearance [2], while in fact the appearance effect of woven fabric is reflected by the surface appearance of yarn.…”

A Review of Yarn Appearance Evaluation Based on Image Analysis Technology

“…For more than 30 years, many research works have been done by using image analysis technology for the quality evaluation of yarns (Barella et al, 1980). Some specific algorithms and software based on image processing and analysis technology have been developed for the measurement of yarn structural parameters, including hairiness (Ozkaya et al, 2008), yarn diameter (Basu et al, 2003;Jaouadi et al, 2009), twist (Jasper et al, 2005;Xu et al, 2008), evenness (Zhang et al, 1998;Liu et al, 2012), thickness (Kang et al, 2001), bulkiness (Cardamone et al, 2002;Behtaj et al, 2011) and etc.…”

Formation of digital yarn black board using sequence images

“…The yarn inspection system in Uster Tester 5-C800 provides the function of simulation of yarn boards. 5 However, this method depends on the testing environmental conditions, because the capacitors are usually affected by the temperature and ambient humidity. The resolution of this method is relatively low where the capacitors sample the data every 8 mm (e.g.…”