Design and Optimization of Deep Convolutional Neural Network for Aircraft Target Classification

Fig. 5. Curves of DCNN training performance by adopting different loss functions. (a) Train accuracy; (b) verification accuracy; (c) train loss; (d) verification loss

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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

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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 1. List of aircraft model parameters
Aircraft type Length /m Height /m Wing span range /m
Boeing 46.61 12.92 44.42
Cessna172 8.28 2.72 11.00
F/A18 17.10 4.70 11.43
AH-64 17.73 3.87 14.63
C-130 29.79 11.66 40.41
MQ-9 11.00 3.80 20.00

Table 2. Classification and loss performances of networks with different number of convolutional layers

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Table 2. Classification and loss performances of networks with different number of convolutional layers

Number ofconvolutional layers	Classification accuracy			Loss
Number ofconvolutional layers	No. 1	No. 2	No. 3	No. 1	No. 2	No. 3
Four	0.889	0.891	0.910	0.49	0.80	0.55
Five	0.893	0.895	0.915	0.51	0.66	0.58
Six	0.877	0.869	0.884	0.83	0.84	0.82
Seven	0.858	0.859	0.870	1.38	1.84	1.30

Table 3. Classification and loss performances for different pooling methods
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Table 3. Classification and loss performances for different pooling methods
Method of pooling Classification accuracy Loss
Max-pooling 0.907 0.65
Average-pooling 0.843 1.25

Table 4. Classification and loss performances for the numbers of neurons and hidden layers in fully connected layer
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Table 4. Classification and loss performances for the numbers of neurons and hidden layers in fully connected layer
Numbers of hiddenlayers and neurons Classificationaccuracy Loss
Two (1024+1024) 0.965 0.15
Two (1024+512) 0.941 0.24
Three (1024+1024+1024) 0.972 0.12
Three (1024+1024+512) 0.978 0.15

Table 5. Classification performances of different optimizers
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Table 5. Classification performances of different optimizers
Optimizer Classification accuracy
SGD 0.978
Adadelta 0.594
RMSprop 0.349
Adam 0.173

Table 6. Classification performances of three methods to reduce overfitting
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Table 6. Classification performances of three methods to reduce overfitting
Method Classification accuracy
BN layer 0.936
Dropout is 0.5 0.912
BN layer,and dropout is 0.5 0.991

Table 7. Comparison of different methods
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Table 7. Comparison of different methods
Method Classification accuracy
AlexNet 0.955
Proposed DCNN 0.991

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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

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