Laser & Optoelectronics Progress, Volume. 57, Issue 10, 101014(2020)
Classification of Bobbins Based on Improved Deep Neural Network
Figures & Tables(14)
Fig. 3. Dataset samples. (a)(b) Black; (c)(d) yellow; (e)(f) green; (g)(h) brown; (i)(j) blackish green; (k)(l) orange; (m)(n) blue; (o)(p) purple
Fig. 7. Convolution feature map. AlexNet model (a) layer 1 feature map, (b) layer 3 feature map, (c) layer 5 feature map; improved model (d) layer 1 feature map, (e) layer 3 feature map, (f) layer 5 feature map
Fig. 8. Accuracy and loss curves of AlexNet model. (a) Training and test accuracy; (b) loss curve
Fig. 9. Accuracy and loss curves of improved model. (a) Training and test accuracy; (b) loss curve
Table 1. Four kinds of network structures based on AlexNet
View tableTable 1. Four kinds of network structures based on AlexNet
Network Layer Conv1 Pooling1 Conv2 Conv3 Pooling2 Conv4 Conv5 Conv6 Pooling3 I 8 3×3 3×3 3×3 3×3 3×3 3×3 3×3 — 3×3 II 8 5×5 3×3 5×5 5×5 3×3 5×5 5×5 — 3×3 III 8 3×3 2×2 3×3 3×3 2×2 3×3 3×3 — 2×2 IV 9 3×3 3×3 3×3 3×3 3×3 3×3 3×3 3×3 3×3
Table 2. Four kinds of network performance
View tableTable 2. Four kinds of network performance
Network I II III IV Training time /h 2.4 2.1 2.7 3.1 Accuracy rate /% 89.31 81.74 85.20 88.42
Table 3. Improved AlexNet model parameters
View tableTable 3. Improved AlexNet model parameters
Layer Layer input Convolution kernel Convolution output Pooling Layer output Size Number Step Size Step Conv1 127×127×3 3×3 64 1 127×127×64 — — 127×127×64 Pooling1 127×127×64 — — — — 3×3 2 63×63×64 Conv2 63×63×64 3×3 128 1 63×63×128 — — 63×63×128 Conv3 63×63×128 3×3 128 1 63×63×128 — — 63×63×128 Pooling2 63×63×128 — — — — 3×3 2 31×31×128 Conv4 31×31×128 3×3 256 1 31×31×256 31×31×256 Conv5 31×31×256 3×3 256 1 31×31×256 — — 31×31×256 Pooling3 31×31×256 — — — — 3×3 2 15×15×256 FC1 15×15×256 — — — — — — 256 FC2 256 — — — — — — 8
Table 4. Impact of optimization techniques on model
View tableTable 4. Impact of optimization techniques on model
Model AlexNet Improved model without technique Improved model with technique Amount of error 56 43 28 Error rate 0.373 0.286 0.186
Table 5. Comparison of recognition performance of different methods
View tableTable 5. Comparison of recognition performance of different methods
Algorithm Training time /h Training accuracy /% Test accuracy /% Number of parameters /M Algorithm of Ref. [4] 0.2 81.6 74.3 — Algorithm of Ref. [7] 3.3 95.3 82.6 25.2 AlexNet 5.4 98.1 61.2 60.3 VGG-16 6.3 99.2 67.7 138.1 Improved algorithm 2.4 91.5 88.2 15.8
Tools
Get Citation
Copy Citation Text
Jian Xu, Shupei Wu, Xiuping Liu. Classification of Bobbins Based on Improved Deep Neural Network[J]. Laser & Optoelectronics Progress, 2020, 57(10): 101014
Paper Information
Category: Image Processing
Received: Aug. 9, 2019
Accepted: Oct. 22, 2019
Published Online: May. 8, 2020
The Author Email: Shupei Wu (1638371002@qq.com)
© Copyright 2018-2021 | Chinese Laser Press.
All Rights Reserved 沪ICP备15018463号-20