Journal of Textile Research ›› 2020, Vol. 41 ›› Issue (02): 69-76.doi: 10.13475/j.fzxb.20181201008

• Textile Engineering • Previous Articles     Next Articles

Method for detecting fluff quality of fabric surface based on BP neural network

JIN Shoufeng1(), LIN Qiangqiang1, MA Qiurui2, ZHANG Hao1   

  1. 1. College of Mechanical and Electrical Engineering, Xi'an Polytechnic University, Xi'an, Shaanxi 710048,China
    2. College of Apparel & Art Design, Xi'an Polytechnic University, Xi'an, Shaanxi 710048, China
  • Received:2018-12-06 Revised:2019-11-20 Online:2020-02-15 Published:2020-02-21

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Abstract:

In order to evaluate objectively the fluff state of fabric surface after the raising process, a method for detecting the surface fluff quality of the fabric based on BP neural network was proposed. The fluff contour image was collected by the principle of optical imaging, and the fluff region was segmented by adaptive image segmentation method. The Freeman chain code principle was applied to the obtained binary image to extract the upper edge contour coordinates of the fabric, and the BP neural network was trained as an input of the BP neural network. The two sets of weights obtained through the training were verified according to the calculation process of the BP neural network, and the method of applying the activation function and the weight of the training combined with the direct calculation was proposed. Applying the fluff fabric detection platform built on the principle of optical imaging, the fabrics processed by four different colors and different fluffing processes were tested. The detection accuracy rate reached 93.02%, and the calculation result of the weight is shown consistent with the actual calculation result of the network. This suggests that the weight of the network training may be used directly for the matrix calculation, which can shorten the actual detection time.

Key words: fabric with fluff, machine vision, BP neural network, optical imaging, raising process

CLC Number: 

  • TN911.73

Fig.1

Principle of light-cut imaging"

Fig.2

Tangential image of fabric"

Fig.3

Grayscale histogram"

Fig.4

Fabric binarization image"

Fig.5

Coordinate extraction of fabric fluff contour edge"

Fig.6

Three-layer BP neural network"

Tab.1

Neural network model analysis"

训练函数 隐含层 输出层 训练时间/s 准确率/%
traingd Log-Sigmoid Log-Sigmoid 1 41.86
Tan-Sigmoid 12 86.05
purelin 13 83.72
Tan-Sigmoid
Log-Sigmoid 1 53.49
Tan-Sigmoid 11 93.02
purelin 4 83.72
traingdm Log-Sigmoid Log-Sigmoid 1 53.49
Tan-Sigmoid 1 60.47
purelin 12 88.37
Tan-Sigmoid
Log-Sigmoid 1 55.81
Tan-Sigmoid 1 60.47
purelin 1 30.23
traingdx Log-Sigmoid Log-Sigmoid 1 55.81
Tan-Sigmoid 1 55.81
purelin 1 88.37
Tan-Sigmoid
Log-Sigmoid 1 72.09
Tan-Sigmoid 1 60.47
purelin 1 86.05
trainb Log-Sigmoid Log-Sigmoid 2 41.84
Tan-Sigmoid 1 60.47
purelin 30 48.84
Tan-Sigmoid
Log-Sigmoid 1 44.84
Tan-Sigmoid 1 55.81
purelin 1 37.21
trainscg Log-Sigmoid Log-Sigmoid 1 67.44
Tan-Sigmoid 1 90.70
purelin 1 74.42
Tan-Sigmoid
Log-Sigmoid 1 76.74
Tan-Sigmoid 1 86.05
purelin 2 88.37
trainr Log-Sigmoid Log-Sigmoid 14 74.42
Tan-Sigmoid 10 83.72
purelin 11 81.40
Tan-Sigmoid
Log-Sigmoid 13 88.37
Tan-Sigmoid 7 83.72
purelin 11 76.74

Fig.7

Part of sample data set. (a)Production inspection of unqualified fabrics; (b)Production inspection qualified fabric"

Fig.8

Actual and predicted values for different combinations. (a)Combination 1; (b)Combination 2; (c)Combination 3;(d)Combination 4"

Fig.9

Fabric after raising process. (a) First raising a#;(b) Final raising b#; (c) Final raising c#;(d) Final raising d#"

Tab.2

Comparison of test results"

织物
类型		 期望输
出值		 BP神经网络
实际输出值		 本文方法
评判结果		 人工
评判
a# 0 0.192 4 不合格 不合格
b# 1 0.987 0 合格 合格
c# 1 0.966 5 合格 合格
d# 1 0.900 5 合格 合格

Tab.3

BP neural network weight verification results"

-0.080 0 -0.021 0 -0.037 0 0.079 5 0.003 5 … -0.007 0 0.059 9
-0.037 0 -0.067 0 0.055 6 0.011 4 -0.069 0 -0.052 0 0.009 0
wmi隐含层权值 -0.040 0 -0.064 0 -0.074 0 -0.040 0 -0.025 0 0.031 3 -0.001 0
?
-0.138 0 -0.021 0 0.083 6 -0.016 0.016 5 0.048 3 0.030 9
b1隐含层阈值 1.473 4 1.222 4 -1.076 0 0.788 0 0.660 1 …… 1.192 5 -1.413 6
wij输出层权值 0.830 7 0.797 6 -0.671 8 -0.465 0.803 3 …… -0.311 0 -0.602 0
b2输出层阈值 -0.006 5

Fig.10

Calculated weight verification"

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