• Laser & Optoelectronics Progress
  • Vol. 56, Issue 10, 101009 (2019)
Dong Zhuo, Junfeng Jing*, Huanhuan Zhang, and Zebin Su
Author Affiliations
  • School of Electronic Information, Xi'an Polytechnic University, Xi'an, Shaanxi 710048, China
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    DOI: 10.3788/LOP56.101009 Cite this Article Set citation alerts
    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 Copy Citation Text show less
    Architecture of convolutional neural network model
    Fig. 1. Architecture of convolutional neural network model
    Training process of convolutional neural network
    Fig. 2. Training process of convolutional neural network
    Flow chart of experiment
    Fig. 3. Flow chart of experiment
    Number of defect samples
    Fig. 4. Number of defect samples
    Dataset samples. (a)-(d) Parallel; (e)-(h) poor dispersion; (i)-(l) yarn knot; (m)-(p) stain
    Fig. 5. Dataset samples. (a)-(d) Parallel; (e)-(h) poor dispersion; (i)-(l) yarn knot; (m)-(p) stain
    Comparison of initialization parameters. (a) Training accuracy; (b) verification accuracy; (c) training loss values; (d) verification loss values
    Fig. 6. Comparison of initialization parameters. (a) Training accuracy; (b) verification accuracy; (c) training loss values; (d) verification loss values
    Training process of fine-tuning. (a) Training accuracy; (b) verification accuracy; (c) training loss values; (d) verification loss values
    Fig. 7. Training process of fine-tuning. (a) Training accuracy; (b) verification accuracy; (c) training loss values; (d) verification loss values
    MethodTrainingaccuracy /%TraininglossVerificationaccuracy /%Verificationloss
    Randomlyinitialized80.00.027075.00.032
    Migrationinitialized99.60.001686.50.023
    Table 1. Comparison of initialization parameters
    Network structureTraining accuracy /%Training lossVerification accuracy /%Verification lossModeling time /s
    Resnet1899.70.001984.60.026350
    Resnet5099.90.001093.00.015949
    Resnet10199.80.005885.60.0221518
    VGG1199.20.001780.00.035940
    VGG1699.40.001386.20.0271629
    VGG1999.60.001686.50.0231952
    Table 2. Model performances
    MatrixPredicted value
    ParallelPoordispersionYarnknotStain
    TruevalueParallel260200
    Poordispersion0257150
    Yarn knot012430
    Stain002260
    Table 3. Confusion matrix of test sample
    Defect categoryPRF1
    Parallel1.000.991.00
    Poor dispersion0.990.940.97
    Yarn knot0.931.000.96
    Stain1.000.991.00
    Table 4. Evaluation of network performances
    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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