Journal of Textile Research ›› 2022, Vol. 43 ›› Issue (06): 133-139.doi: 10.13475/j.fzxb.20210602607

• Dyeing and Finishing & Chemicals • Previous Articles     Next Articles

An improved AlexNet model for fleece fabric quality inspection

JIN Shoufeng1,2(), HOU Yize1,2, JIAO Hang1,2, ZHNAG Peng1,2, LI Yutao1,2   

  1. 1. College of Mechanical and Electrical Engineering, Xi'an Polytechnic University, Xi'an, Shaanxi 710600, China
    2. Xi'an Key Laboratory of Modern Intelligent Textile Equipment, Xi'an Polytechnic University, Xi'an, Shaanxi 710600, China
  • Received:2021-06-08 Revised:2022-03-04 Online:2022-06-15 Published:2022-07-15

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Abstract:

The traditional image recognition method is difficult to extract the surface features of the fleece fabrics leading to low recognition accuracy. This study proposed an improved AlexNet model for the quality detection method of the fleece fabrics. The convolutional neural network was used to extract the sample features of the fleece fabric, and the experimental method combining SGDM, RMSProp, Adam optimization algorithm was adopted for the study to investigate effects of changing learning rate and the use of two new learning and transfer learning algorithms in training the fleece fabric image dataset. After the completion of training, different depth pooling layers of the convolutional neural network were employed to extract the features of the fleece fabric samples. The extracted fleece fabric features were fitted to the support vector machine(SVM) classifier to analyze the input fleece fabric image. The experimental results show that the use of the convolutional neural network method can increase the ability of the convolutional layer to extract the surface features of the fleece fabric, and obtain image features with higher resolution. The model trained by the data enhancement and SGDM algorithm can extract the network pool5 layer features. With the SVM classifier, the recognition accuracy enhanced significantly. The quality detection method of fleece fabrics based on the improved AlexNet model can extract the surface features of fleece fabrics with high recognition rate.

Key words: fleece fabric, machine vision, convolutional neural network, transfer learning, data enhancement, fabric quality inspection

CLC Number: 

  • TN911.73

Fig.1

Example of scratching image. (a) Wool catching fabric A;(b) Wool catching fabric B;(c) Wool catching fabric C;(d) Wool catching fabric D;(e) Wool catching fabric E;(f) Wool catching fabric F"

Fig.2

Data expansion. (a) Original drawing;(b) Image scaling;(c) Noise image"

Tab. AlexN

et network parameter table"

名称 说明 参数总数
Data(图像输入) 227×227×3图像 -
Conv1(卷积) 核数:96 11×11×3卷积
步幅[4 4]填充[0 0 0 0]		 34 944
Conv2(卷积) 核数:2组1 285×5×48卷积
步幅[1 1]填充[2 2 2 2]		 307 456
Conv3(卷积) 核数:3 843×3×256卷积
步幅[1 1]填充[1 1 1 1]		 88 5120
Conv4(卷积) 核数:2组1 923×3×192卷积
步幅[1 1]填充[1 1 1 1]		 663 936
Conv5(卷积) 核数:2组1 283×3×192卷积
步幅[1 1]填充[1 1 1 1]		 442 624
FCL6(全连接) 4 098全连接层 3 775 2832
FCL7(全连接) 4 098全连接层 1 6781 312
FCL8(全连接) 1 000全连接层 4 097 000
Output(分类输出) - -

Fig.3

Schematic diagram of improved model"

Fig.4

Flow chart of wool catching fabric recognition method"

Fig.5

Fleece fabric image outputs feature image in the CL2 layer of the AlexNet network.(a)Compact uniformity of original image;(b)Compact uniformity of feature image;(c)Sparse and uneven of original image;(d)Sparse and uneven of feature image"

Fig.6

Visualization of convolution layer features"

Tab.1

Recognition results by different models"

训练
方式		 训练优
化算法		 学习率 过拟
合率		 训练平均
耗时/min		 测试集准确率/%
抓毛织物A 抓毛织物B 抓毛织物C 抓毛织物D 抓毛织物E 抓毛织物F
全新
学习		 SGDM 0.000 1
0.001		 1.013
1.019		 31
27		 88.35
75.21		 82.11
70.25		 89.35
76.31		 88.52
75.35		 88.37
77.97		 86.24
79.24
RMSProp 0.000 1
0.001		 1.028
1.032		 36
33		 83.58
79.35		 83.64
75.24		 88.57
82.15		 82.35
70.37		 86.17
75.64		 87.68
80.79
Adam 0.000 1
0.001		 1.123
1.106		 37
35		 85.25
73.03		 76.35
77.27		 84.38
80.65		 81.25
80.02		 83.68
80.42		 84.13
82.47
迁移
学习		 SGDM 0.000 1
0.001		 1.010
1.015		 21
18		 99.85
98.75		 98.28
98.35		 99.34
97.35		 99.98
98.27		 99.98
99.87		 99.43
98.94
RMSProp 0.000 1
0.001		 1.016
1.020		 27
23		 95.36
94.49		 92.41
91.68		 91.27
94.35		 91.26
88.34		 93.06
92.01		 92.15
91.56
Adam 0.000 1
0.001		 1.113
1.119		 28
26		 92.35
90.35		 90.35
91.25		 91.25
74.21		 89.08
92.25		 91.02
87.82		 94.25
89.54

Fig.7

Comparison of classification success rates using different layer features"

Fig.8

Impact of data enhancement on model"

Tab.2

Comparison and recognition results of learning styles"

学习方式 数据
增强		 学习率 pool5层的输出拟合SVM分类器抓毛织物表面测试准确率/% 平均识别
率/%
抓毛织物A 抓毛织物B 抓毛织物C 抓毛织物D 抓毛织物E 抓毛织物F
全新学习 是 0.000 1 98.12 99.02 98.96 98.14 97.08 98.89 98.37
迁移学习 是 0.000 1 99.37 99.08 99.13 99.09 99.75 99.54 99.33

Fig.9

Influence of different learning methods on model"

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