Journal of Textile Research ›› 2021, Vol. 42 ›› Issue (02): 101-106.doi: 10.13475/j.fzxb.20201008406

• Textile Engineering • Previous Articles     Next Articles

Detection of fabric density uniformity based on convolutional neural network

MENG Shuo, XIA Xuwen, PAN Ruru(), ZHOU Jian, WANG Lei, GAO Weidong   

  1. Key Laboratory of Eco-Textiles (Jiangnan University), Ministry of Education,Wuxi, Jiangsu 214122, China
  • Received:2020-10-30 Revised:2020-11-11 Online:2021-02-15 Published:2021-02-23
  • Contact: PAN Ruru E-mail:prrsw@163.com

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Abstract:

Aiming at the narrow field of vision, low precision, and low adaptability in the current automatic methods for measuring fabric density, a multi-scale convolutional neural network was proposed. An offline image acquisition system was designed to acquire fabric images continuously, and a fabric image dataset containing detailed fabric parameters was established. Then, a multi-scale convolutional neural network with different sizes of local receptive fields was used for dealing with different fabric structure features and for locating yarns. The Hough transform and gray projection method were used to process the predicted yarn position map in order to calculate the warp and weft density and evaluate the fabric density uniformity. The results show that, compared with other methods, the detecting error of the warp and weft densities for different types of fabrics is less than 2%, which indicates that the proposed method has a higher accuracy and stronger variety adaptability.

Key words: woven fabric, fabric density, fabric structural parameter, image processing, convolutional neural network

CLC Number: 

  • TS101.92

Fig.1

Image acquisition system. (a) Diagram of system;(b) Camera module"

Fig.2

Distribution of warp and weft densities and fabric types in dataset"

Fig.3

Original image with labeled yarn location(a) and generated warp location map(b)"

Fig.4

Structure of neural network"

Fig.5

Some representative fabric images and predicted location maps in test set. (a) Plain yarn-dyed fabric; (b) Left twill fabric; (c) Right twill yarn-dyed fabric; (d) Plain fabric; (e) Satin yarn-dyed fabric; (f) Jacquard yarn-dyed fabric"

Tab.1

Comparison between manual measurements and proposed method in Fig. 5"

织物
编号		 自动检测密度/
(根·(10 cm)-1)		 人工检测密度/
(根·(10 cm)-1)		 误差/%
经密 纬密 经密 纬密 经密 纬密
a 456.18 312.20 453 315 0.70 0.89
b 585.16 430.71 591 433 0.99 0.53
c 583.74 492.17 591 492 1.23 0.03
d 510.79 412.60 512 413 0.24 0.10
e 675.08 400.51 669 394 0.91 1.65
f 615.79 400.08 630 394 2.26 1.54

Tab.2

Testing errors of different parameters"

参数设置 MAPE/% MSPE/%
经密 纬密 经密 纬密
σ=1 1.52 2.12 2.44 2.78
σ=0.1 1.90 2.49 4.50 4.88
φ=1 2.90 3.68 6.20 6.90
φ=0.1 1.42 1.71 2.04 2.68
φ=0.01 2.41 2.17 5.31 4.92
λ=0 2.52 2.34 5.58 5.89
λ=0.01 1.59 1.67 2.51 3.51
λ=0.001 1.42 1.71 2.04 2.68
λ=0.0001 1.91 2.11 3.95 4.04

Tab.3

Testing errors of different methods"

方法名称 MAPE/% MSPE/%
经密 纬密 经密 纬密
灰度投影 8.44 10.28 15.24 19.20
MSnet 1.55 1.80 3.33 3.31
改进MSnet 1.42 1.71 2.04 2.68
[1] ZHANG Jie, GAO Weidong, PAN Ruru, et al. A backlighting method for accurate inspection of woven fabric density[J]. Industria Textila, 2017,68(1):31-36.
[2] ZHANG Rui, XIN Binjie. An investigation of density measurement method for yarn-dyed woven fabrics based on dual-side fusion technique[J]. Measurement Science & Technology, 2016,27:085403.
[3] XIANG Zhong, CHEN Kaifeng, QIAN Miao, et al. Yarn-dyed woven fabric density measurement method and system based on multi-directional illumination image fusion enhancement technology[J]. Journal of the Textile Institute, 2019(1):1-13.
[4] 姚穆. 纺织材料学[M]. 3版. 北京: 中国纺织出版社, 2010: 222-223.
YAO Mu. Textile materials[M]. 3rd ed. Beijing: China Textile & Apparel Press, 2010: 222-223.
[5] SCHNEIDER D, GLOY Y S, MERHOF D. Vision-based on-loom measurement of yarn densities in woven fabrics[J]. IEEE Transactions on Instrumentation & Measurement, 2015,64:1063-1074.
[6] 王庆涛, 马崇启, 高雨田, 等. 基于频域特征点提取的素色机织物密度识别算法[J]. 纺织学报, 2014,35(4):47-51.
WANG Qingtao, MA Chongqi, GAO Yutian, et al. Identification algorithm of plain woven fabric density via feature point extraction in frequency domain[J]. Journal of Textile Research, 2014,35(4):47-51.
[7] 何峰, 李立轻, 徐建明. 基于自适应小波变换的织物密度测量[J]. 纺织学报, 2007,28(2):32-35.
HE Feng, LI Liqing, XU Jianming. Woven fabric density measure based on adaptive wavelets trans-form[J]. Journal of Textile Research, 2007,28(2):32-35.
[8] 潘如如, 高卫东. 基于图像处理的机织物密度的高精度识别[J]. 纺织学报, 2008,29(11):128-131.
PAN Ruru, GAO Weidong. High-precision identification of woven fabric density via image processing[J]. Journal of Textile Research, 2008,29(11):128-131.
[9] PAN Ruru, GAO Weidong, LIU Jinhong, et al. Automatic inspection of woven fabric density of solid colour fabric density by the Hough transform[J]. Fibres & Textiles in Eastern Europe, 2010,18:81.
[10] LIN J. Applying a co-occurrence matrix to automatic inspection of weaving density for woven fabrics[J]. Textile Research Journal, 2002,72:486-490.
[11] MENG Shuo, PAN Ruru, GAO Weidong, et al. Woven fabric density measurement by using multi-scale convolutional neural networks[J]. IEEE Access, 2019; 7:75810-75821.
[12] SZEGEDY C, LIU W, JIA Y, et al. Going deeper with convolutions[C]// REED S. IEEE Conference on Computer Vision and Pattern Recognition. Boston: arXiv, 2015: 1-9.
[13] RONNEBERGER O, FISCHER P, BROX T. U-Net: convolutional networks for biomedical image segmentation[C]// BROX T. International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer, 2015: 234-241.
[14] WANG Z, BOVIK A C, SHEIKH H R, et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Trans Image Process, 2004,13(4):600-612.
[15] OHTSU N. A threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems Man & Cybernetics, 1979,9:62-66.
[16] ZHANG Y T, SUEN C Y. A fast parallel algorithm for thinning digital patterns[J]. Comm Acm, 1984,27:236-239.
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