Journal of Textile Research ›› 2019, Vol. 40 ›› Issue (11): 50-56.doi: 10.13475/j.fzxb.20181104307

• Textile Engineering • Previous Articles     Next Articles

Objective smoothness evaluation of fabric based on Fourier spectral features

SHI Kangjun, WANG Jing'an, GAO Weidong()   

  1. Key Laboratory of Eco-Textiles (Jiangnan University), Ministry of Education, Wuxi, Jiangsu 214122, China
  • Received:2018-11-19 Revised:2019-03-28 Online:2019-11-15 Published:2019-11-26
  • Contact: GAO Weidong E-mail:gaowd3@163.com

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Abstract:

In order to solve the problems that the manual evaluation of fabric smoothness is subjective and the existing objective evaluation has low accuracy, a fabric smoothness evaluation method based on Fourier transform, spectral feature extraction and support vector machine was proposed. Firstly, the images of the standard replicas and fabric samples were acquired; the obtained images were preprocessed and transformed to the Fourier frequency domain; and a set of low-pass filters were constructed in the frequency domain, and the frequency interval of the wrinkle information in the spectrogram was determined by frequency domain filtering and inverse Fourier transformation, which is called the wrinkle contribution interval. The wrinkle contribution interval was divided into several feature subintervals, and the spectral amplitude in each interval was integrated, which is constructed into the feature vector. The feature vector set was constructed by the feature vectors of all training samples, and utilized to train the support vector machine, which can objectively evaluate the fabric smoothness. The total number of the adopted fabric samples is 132, among which 24 and standard template images are taken as the training set, and the rest are taken as the test set. The results show that the algorithm performs well on fabric smoothness evaluation, and the evaluation accuracy rate is up to 96.30%.

Key words: smoothness, objective evaluation, fabric wrinkle, Fourier transform, support vector machine

CLC Number: 

  • TS941.2

Fig.1

System of capturing sample"

Fig.2

Images of AATCC template"

Fig.3

Contrast diagram before and after equalization. (a) Image before equalization;(b) Image after equalization"

Fig.4

Transformed images of AATCC template by 2D-FFT"

Fig.5

Spectrum amplitude total of AATCC template"

Fig.6

Each filter and its inverse transformed image"

Fig.7

Spectra amplitude total of each feature subinterval"

Fig.8

Schematic diagram of fabric amplitude distribution of each grade"

Fig.9

Comparison of different resolutions fabric spectra. (a) High resolution twill fabrics and local magnification; (b) Fabric spectrum of (a); (c) Low resolution twill fabrics and local magnification; (d) Fabric spectrum of (c)"

Tab.1

Classification accuracy of different segmentation steps"

分割步长 特征子区间数 分类准确率/% 训练平均时间/s
5 36 85.19 1.774 1
7 15 88.89 1.628 6
9 10 88.89 1.606 7
11 6 96.30 1.589 8
13 6 92.60 1.580 6
15 3 94.44 1.579 2

Tab. 2

Classification accuracy of different pretreatment conditions"

预处理方式 分类准确率/% 支持向量机参数
[0, 1]归一化 93.52 ‘-t 1 -c 1 -g 1/6’
[-1, 1]归一化 39.81 ‘-t 1 -c 1 -g 1/6’
原始数据 81.48 ‘-t 1 -c 1 -g 1/6’

Tab.3

Classification accuracy of different parameter selection"

组号 惩罚系数c 核函数宽度g 分类准确率/%
1 1.00 0.170 0 93.52
2 2.78 0.002 6 38.89
3 0.04 0.620 0 25.93
4 0.23 0.091 0 96.30

Fig.10

Images of fabric for different light angles"

Tab.4

Classification accuracy of different light angles"

光源角度/(°) 0 45 90 135 180 225 270 315
准确率/% 96.30 94.44 85.19 88.89 92.59 88.89 72.22 70.37

Tab.5

Classification accuracy of different light intensities and different incident elevation angles%"

光照强度/lx 光源入射高度角/(°)
50 100 150 200 250 0 27 45 56 63 68
88.89 88.89 94.44 96.30 92.60 24.07 96.30 16.67 9.26 9.26 9.26
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