## 4 Discussion
### Comparative Experiment 2: Comparing the SRS Images with the Real Scene Ship Image  
The similarity between the SRS images and the real ship image is the key of the study. We selected a real ship image and an SRS image with the same background to detect the similarity between both. Figure 13a is a real ship image, and Figure 13b is an SRS image. We took pixels as the research object for modeling. The average value of each row of pixels in the two images was calculated (Figure 13c). The results show that the designed SRS images have a high similarity with the real ship image.  
In order to further verify the effectiveness of the SRS image, we took two kinds of data, which are real ship images and SRS images, as training data and took real images as testing data to carry out experiments. The experiment included three steps: first, all the training data were real images. Then, we took 300 real images and 200 simulation images as training data. Finally, we took 100 real images and 400 simulation images as training data. Taking Mask RCNN and FCN as training and testing methods, the experimental design and results are shown in Table 1.  
Table 1 shows the results of different detection methods combined with various image dataset. According to the results of FCN, \"SRS-I(400) + Real(100)\" achieves the highest accuracy rate (91.3%), followed by \"SRS-I(200) + Real(300)\" (88.5%), \"Real(500)\" (84.2%), \"SRS-I(400) + Real(100)\" (TPR: 92.8% and FPR: 92.%) 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, \"SRS-I(400) + Real(100)\" demonstrated the highest AUC (0.910). Therefore, the dataset generated by the proposed method was more effective than the real dataset selected by us (Figure 14). The detection results of Mask RCNN, \"SRS-I(400) + Real(100)\" achieved the  
\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} & Real(500) & & 84.2 & 86.4 & 19.1 \\  & Real(300) + SRS-I(200) & Real(500) & 88.5 & 90.8 & 11.8 \\  & Real(100) + SRS-I(400) & & 91.3 & 92.8 & 9.2 \\ \hline \multirow{3}{*}{Mask RCNN} & Real(500) & & 86.3 & 88.5 & 17.6 \\  & Real(300) + SRS-I(200) & Real(500) & 90.6 & 93.2 & 10.6 \\ \cline{1-1}  & Real(100) + SRS-I(400) & & 92.9 & 94.5 & 7.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test results of similar data sets.  
Figure 13: Difference between the SRS image and the real ship image: (**a**) the real ship image; (**b**) SRS images; (**c**) contrast curve.  
highest accuracy rate (92.9%), followed by \"SRS-I(200) + Real(300)\" (90.6%), \"Real(500)\" (86.3%).  
\"SRS-I(400) + Real(100)\" (TPR: 94.5% and FPR: 7.6%) 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 (0.918) (see Figure 14).