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
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