## 4 Discussion
### Comparative Experiment 3: Comparison with the Existing Data Augmentation Methods  
At present, the common data augmentation methods, such as Imgaug [41], have the core idea of pixel transformation of 2D images. The transformation methods, such as rotation, translation, flipping, scaling, and clipping, increased the number of samples.  
The target detection algorithm based on deep learning needs many image data for training, extracting sufficient target features from the image data, and generating the target detector. The abundant target features extracted from the training data guaranteed the detection accuracy. Traditional data enhancement methods increase the amount of data by changing the scale, posture and color of the existing 2D image but the actual enhancement of image features is limited.  
GAN [29], an unsupervised deep learning algorithm, is a classic data augmentation method. GAN aims to fit the distribution of the sample set to obtain highly qualified samples based on zero-sum game theory. The GAN algorithm generated simulation images highly similar to the real images. The simulation ship images generated by CycleGAN were used for deep learning training and testing in this paper. The simulation ship generated by the CycleGAN algorithm [30] after 500 epochs is shown in Figure 15a. The SRS image shown in Figure 15c has a much higher definition and clearer contour than the image shown in Figure 15a.  
Figure 14: AUC of the selected methods (algorithm index corresponds to the No. shown in Table 1).  
Table 2 shows the results of different detection methods combined with various image datasets. According to the results of FCN, the proposed method achieved the highest accuracy rate (93.2%), followed by CycleGAN (90.9%), Imgaug (85.7%). The proposed method (TPR: 94.4% and FPR: 7.1%) outperformed the compared methods, as confirmed by the results of the true positive rate (TPR) and false positive rate (FPR). The FPR of the proposed method was the lowest among the compared methods. Moreover, the proposed method demonstrated the highest AUC (0.924). Therefore, the dataset generated by the proposed method was more effective than other compared datasets (Figure 16). The detection results of Mask RCNN, the proposed method achieved the highest accuracy rate (94.6%), followed by CycleGAN (91.7%) and Imgaug (87.3%).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Training and** & **Type of Data for Training** & **Type of Data for Testing (Number)** & **Accuracy (\%)** & **TPR (\%)** & **FPR (\%)** \\ \hline \multirow{3}{*}{FCN} & Imgaug [41] & \multirow{3}{*}{Real(500)} & 85.7 & 88.9 & 17.1 \\  & CycleGAN [30] & & 90.9 & 91.5 & 11.2 \\  & Our method & & 93.2 & 94.4 & 7.1 \\ \hline \multirow{3}{*}{Mask RCNN} & Imgaug [41] & \multirow{3}{*}{Real(500)} & 87.3 & 89.5 & 14.6 \\  & CycleGAN [30] & & 91.7 & 93.1 & 10.5 \\ \cline{1-1}  & Our method & & 94.6 & 95.7 & 5.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Test results of similar data sets.  
Figure 16: AUC of the selected methods (algorithm index corresponds to the no. shown in Table 2).  
Figure 15: The simulation ship image generated by different methods: (**a**) the simulation ship image generated by CycleGAN; (**b**) the real ship image; (**c**) the SRS image.  
The proposed method (TPR: 95.7% and FPR: 5.6%) outperformed the compared methods according to the true positive rate (TPR) and false positive rate (FPR). The FPR of the proposed method was the lowest among the compared methods. Therefore, the dataset generated by the proposed method was more effective than the other compared datasets, as confirmed by the highest AUC (0.935) (see Figure 16).