Underwater Acoustic Target Recognition Based on Data Augmentation and Residual CNN
Abstract
:1. Introduction
2. MFCC
3. Data Augmentation
3.1. Traditional Methods
3.2. DCGAN
4. Theory of Classification Models Used
4.1. SVM
4.2. CNN
4.2.1. Theoretical Basis
4.2.2. Residual Connection Model
4.2.3. CNN Model Construction
5. Analysis and Verification of Experimental Data
5.1. Extraction of Input Features
5.2. Expansion of Samples by Data Augmentation
5.3. Recognition Results
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Category | Specific Number, Name, and Recording Time |
---|---|
Tug | 9, MILLENNIUMSTAR, 20171115 40, SEASPAN COMMANDER, 20171203 49, SEASPAN EAGLE, 20171210 |
Cargo | 15, NEW NADA, 20171114 38, ARIES LEADER, 20171123 62, ANASTASIA, 20171202 |
Tanker | 10, NAVE ORBIT, 20160602 18, OVERSEAS ATHENS, 20160619 24, HIGH ENDURANCE, 20160710 |
Passenger | 9, CARNIVAL LEGEND, 20160516 16, MALASPINA, 20160604 29, CRYSTAL SERENITY, 20160727 |
Method | A | B | C | D | Total |
---|---|---|---|---|---|
SVM | 78.55 | 78.38 | 79.92 | 77.85 | 78.68 |
SVM_Aug | 80.29 | 81.31 | 80.96 | 79.32 | 80.47 |
3_CNN | 83.18 | 81.91 | 82.35 | 81.03 | 82.12 |
3_CNN_Aug | 88.95 | 86.21 | 87.72 | 85.52 | 87.10 |
VGG19 | 85.19 | 87.35 | 86.03 | 86.93 | 86.38 |
VGG19_Aug | 87.24 | 89.52 | 89.15 | 90.39 | 89.08 |
ResNet18 | 93.11 | 92.24 | 92.98 | 91.62 | 92.48 |
ResNet18_Aug | 96.85 | 96.23 | 97.01 | 95.38 | 96.37 |
Method | Accuracy | Precision | Recall | F1 Score |
---|---|---|---|---|
SVM | 78.68 | 78.76 | 78.61 | 78.68 |
SVM_Aug | 80.47 | 80.58 | 80.42 | 80.50 |
3_CNN | 82.12 | 82.15 | 82.10 | 82.13 |
3_CNN_Aug | 87.10 | 87.15 | 87.13 | 87.14 |
VGG19 | 86.38 | 86.42 | 86.40 | 86.41 |
VGG19_Aug | 89.08 | 89.11 | 89.07 | 89.09 |
ResNet18 | 92.48 | 92.49 | 92.47 | 92.48 |
ResNet18_Aug | 96.37 | 96.40 | 96.39 | 96.40 |
Method | Accuracy | Precision | Recall | F1 Score |
---|---|---|---|---|
SVM | 72.56 | 72.61 | 72.52 | 72.57 |
SVM_Aug | 76.33 | 76.40 | 76.32 | 76.36 |
3_CNN | 79.61 | 79.68 | 79.59 | 79.64 |
3_CNN_Aug | 83.89 | 83.95 | 83.80 | 83.87 |
VGG19 | 81.08 | 81.19 | 81.05 | 81.12 |
VGG19_Aug | 85.23 | 85.24 | 85.17 | 85.21 |
ResNet18 | 87.35 | 87.36 | 87.32 | 87.34 |
ResNet18_Aug | 91.92 | 91.96 | 91.90 | 91.93 |
Method | Accuracy | Precision | Recall | F1 Score |
---|---|---|---|---|
SVM | 68.12 | 68.18 | 68.10 | 68.14 |
SVM_Aug | 71.41 | 71.43 | 71.37 | 71.40 |
3_CNN | 75.03 | 75.05 | 74.98 | 75.02 |
3_CNN_Aug | 77.45 | 77.46 | 77.42 | 77.44 |
VGG19 | 78.01 | 78.07 | 77.99 | 78.03 |
VGG19_Aug | 81.52 | 81.55 | 81.48 | 81.52 |
ResNet18 | 86.29 | 86.33 | 86.27 | 86.30 |
ResNet18_Aug | 88.49 | 88.52 | 88.48 | 88.50 |
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Yao, Q.; Wang, Y.; Yang, Y. Underwater Acoustic Target Recognition Based on Data Augmentation and Residual CNN. Electronics 2023, 12, 1206. https://doi.org/10.3390/electronics12051206
Yao Q, Wang Y, Yang Y. Underwater Acoustic Target Recognition Based on Data Augmentation and Residual CNN. Electronics. 2023; 12(5):1206. https://doi.org/10.3390/electronics12051206
Chicago/Turabian StyleYao, Qihai, Yong Wang, and Yixin Yang. 2023. "Underwater Acoustic Target Recognition Based on Data Augmentation and Residual CNN" Electronics 12, no. 5: 1206. https://doi.org/10.3390/electronics12051206