Laser & Optoelectronics Progress, Volume. 57, Issue 24, 241011(2020)
Digital Printing Defect Classification Algorithm Based on Convolutional Neural Network
Figures & Tables(15)
Fig. 1. Examples of digital printing defects. (a) PASS tracks; (b) uneven inkjet; (c) ink leakage; (d) fabric wrinkles
Fig. 2. RGB color space histogram equalization processing results. (a) PASS tracks; (b) uneven inkjet; (c) ink leakage; (d) fabric wrinkles
Fig. 3. Gaussian filtering processing results. (a) PASS tracks; (b) uneven inkjet; (c) ink leakage; (d) fabric wrinkles
Fig. 4. Adjustment results of image resolution based on local mean algorithm. (a) Before resolution adjustment; (b) after resolution adjustment
Fig. 5. Image data enhancement results. (a) Original image; (b) flip vertically; (c) horizontal mirroring; (d) rotate 90°; (e) rotate 180°; (f) rotate 270°
Fig. 8. Samples of digital printing defect data set. (a)--(d) PASS tracks; (e)--(h) uneven inkjet; (i)--(l) ink leakage; (m)--(p) fabric wrinkles
Table 1. Comparison of defect features in digital printing
View tableTable 1. Comparison of defect features in digital printing
Type of defect Cause of formation Appearance shape Probability of occurrence PASS tracks Nozzle clogging,motor step deviation Narrow linear High Uneven inkjet Uneven inkjet output debugging Flat Low Ink leakage Inkjet pressure instability Dotted Medium Fabric wrinkles Uneven cloth press Strip Low
Table 2. Classification accuracy corresponding to different objective functions
View tableTable 2. Classification accuracy corresponding to different objective functions
Objective function Accuracy/% Softmax cross entropy 98.14 Classification cross entropy 96.42 Binary cross entropy 81.29 Mean square loss 88.02 Hinge loss 74.92 ROC AUC score 77.33
Table 3. Classification accuracy corresponding to different optimization algorithms
View tableTable 3. Classification accuracy corresponding to different optimization algorithms
Optimization Accuracy/% Adaptive moment estimation 98.21 Stochastic gradient descent 74.84 Root mean square propagation 65.38 Momentum gradient descent 92.73 Adaptive sub-gradient method 81.67
Table 4. Performance index of each defect classification
View tableTable 4. Performance index of each defect classification
Defect classification Performance /% Average accuracy /% Standard deviation 1 2 3 4 5 6 7 8 9 10 Validation set 98.17 98.53 96.33 95.00 98.33 96.17 95.61 98.41 95.27 96.18 96.80 0.0133 Test set PASS tracks 92 94 89 95 85 93 86 90 88 91 90.30 0.0316 Uneven inkjet 94 98 97 96 91 89 92 92 93 90 93.20 0.0286 Ink leakage 98 100 93 97 94 95 100 98 96 97 96.80 0.0223 Fabric wrinkles 100 93 96 95 98 96 97 94 95 94 95.80 0.0199
Table 5. Training and testing time of different CNN models
View tableTable 5. Training and testing time of different CNN models
CNN model LeNet5 AlexNet VGG16 GoogLeNet Proposed Training/min 76 92 114 136 65 Testing/ms 15 64 153 124 10
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Zebin Su, Min Gao, Pengfei Li, Junfeng Jing, Huanhuan Zhang. Digital Printing Defect Classification Algorithm Based on Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2020, 57(24): 241011
Paper Information
Category: Image Processing
Received: Apr. 27, 2020
Accepted: Jun. 9, 2020
Published Online: Dec. 9, 2020
The Author Email: Su Zebin (suzebin@xpu.edu.cn)
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