Classification of Chopped Strand Mat Defects Based on Convolutional Neural Network

Fig. 6. Comparison of initialization parameters. (a) Training accuracy; (b) verification accuracy; (c) training loss values; (d) verification loss values

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Fig. 7. Training process of fine-tuning. (a) Training accuracy; (b) verification accuracy; (c) training loss values; (d) verification loss values

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Table 1. Comparison of initialization parameters
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Table 1. Comparison of initialization parameters
Method Trainingaccuracy /% Trainingloss Verificationaccuracy /% Verificationloss
Randomlyinitialized 80.0 0.0270 75.0 0.032
Migrationinitialized 99.6 0.0016 86.5 0.023

Table 2. Model performances

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Table 2. Model performances

Network structure	Training accuracy /%	Training loss	Verification accuracy /%	Verification loss	Modeling time /s
Resnet18	99.7	0.0019	84.6	0.026	350
Resnet50	99.9	0.0010	93.0	0.015	949
Resnet101	99.8	0.0058	85.6	0.022	1518
VGG11	99.2	0.0017	80.0	0.035	940
VGG16	99.4	0.0013	86.2	0.027	1629
VGG19	99.6	0.0016	86.5	0.023	1952

Table 3. Confusion matrix of test sample
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Table 3. Confusion matrix of test sample
Matrix Predicted value
Parallel Poordispersion Yarnknot Stain
Truevalue Parallel 260 2 0 0
Poordispersion 0 257 15 0
Yarn knot 0 1 243 0
Stain 0 0 2 260

Table 4. Evaluation of network performances
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Table 4. Evaluation of network performances
Defect category P R $\begin{matrix} F_{1} \end{matrix}$
Parallel 1.00 0.99 1.00
Poor dispersion 0.99 0.94 0.97
Yarn knot 0.93 1.00 0.96
Stain 1.00 0.99 1.00

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Dong Zhuo, Junfeng Jing, Huanhuan Zhang, Zebin Su. Classification of Chopped Strand Mat Defects Based on Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2019, 56(10): 101009

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