Juncheng Ma, Hongdong Zhao, Dongxu Yang, Qing Kang. Design and Optimization of Deep Convolutional Neural Network for Aircraft Target Classification[J]. Laser & Optoelectronics Progress, 2019, 56(23): 231006
- Laser & Optoelectronics Progress
- Vol. 56, Issue 23, 231006 (2019)
Fig. 1. Six types of aircraft targets are used. (a) Boeing; (b) Cessna172; (c) F/A18; (d) AH-64; (e) C-130; (f) MQ-9
Fig. 2. Effect of aircraft mirroring operation
Fig. 3. Effect of aircraft rotation operation
Fig. 4. Structure of proposed deep convolutional neural network
Fig. 5. Curves of DCNN training performance by adopting different loss functions. (a) Train accuracy; (b) verification accuracy; (c) train loss; (d) verification loss
Fig. 6. Comparison between train_loss and val_loss. (a) Adding BN layers; (b) dropout is 0.5; (c) dropout is 0.5, and BN layers are added
Fig. 7. Normalized confusion matrix of the proposed DCNN architecture for aircraft classification
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Table 1. List of aircraft model parameters
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Table 2. Classification and loss performances of networks with different number of convolutional layers
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Table 3. Classification and loss performances for different pooling methods
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Table 4. Classification and loss performances for the numbers of neurons and hidden layers in fully connected layer
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Table 5. Classification performances of different optimizers
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Table 6. Classification performances of three methods to reduce overfitting
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Table 7. Comparison of different methods
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