## 3 Proposed Diverse and Realistic Marine Image Generation Framework
### Discriminator  
We employed multi-scale discriminators introduced in pix2 pixHD [24] to update the generators such that they produced both realistic local features and correct global structures. A discriminator with a large receptive field is required to produce high-quality images. Generally, the network depth or the kernel size increases. However, in such cases, substantial memory is required for training, and overfitting may occur. Therefore, a multi-scale discriminator with the same structure, but managing different image scales, was used. Figure 5: Flowchart depicting horizon-targeted loss calculation process. ```
1:Input:\(Image_{virtual/real}\), \(Image_{realistic}\)
2:Output:\(Horizon\) targeted loss
3:\(minY\gets Image\)Height
4:\(detectedObjectList\leftarrow\) Yolo_Based_Object_Detection_Network(\(Image_{virtual}\))
5:If\(detectedObjectList<\textit{>}\ null\)then
6:For\(i:=0\to len(detectedObjectList)\)do
7:If\(object\==^{\prime}\) \(essel\)then
8:\(esselList.append(detectedObjectList[i])\)
9:\(Ro_{\textit{l}essel}list\leftarrow\) RolvesselListExtract(\(vesselList\))
10:If\(detectedObjectList[i].y<\textit{minY}\)then
11:\(minY\gets DetectedObjectList[i].y\)
12:End
13:End
14:\(Ro_{\textit{l}horizon}\leftarrow\) Rolv horizonExtract(\(minY\))
15:\(grayImage_{\textit{virtual/real}}\leftarrow\) Convert_to_gray(\(Image_{\textit{virtual/



### Evaluation Metrics

We utilized a Frechet Inception Distance (_FID_) [25] in our evaluation, which is a popular metric for evaluating image generation tasks. This metric calculates the distance between feature vectors calculated for real and generated images. The _FID_ metrics were defined as follows:

\[FID=\parallel\mu_{X}-\mu_{Y}\parallel^{2}+\text{Tr}\left(\sum X+\sum Y-2\sqrt{ \sum X\sum Y}\right) \tag{7}\]

### Quantitative Evaluation of Diverse Photorealistic Marine Image Generator

Figure 7 illustrates the results of converting domains A -> B and B -> A. The figure reveals that the generator can maintain the structures within each image, such as the horizon, ship, and mountain shapes, on conversion. The trained network is one, and we can generate new diverse style images. We applied FID to quantitatively evaluate the performance of the proposed method. To obtain a lower score, a model must be able to generate images that are similar with real world image data. While virtual images, which obtained by our simulator yielded an FID of 109.422, the realistic images which are generated by the proposed method yielded an FID of 102.210. Table 3 shows our results are closer to the real data than those of virtual images. Figure 8 illustrates the results of converting the virtual ocean simulator images of four frames into four different styles. The results indicated that the styles of the sky and sea were well applied. \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **FID (\(\downarrow\))** \\ \hline Virtual images vs. Real images & 109.422 \\ \hline Realistic images (ours) vs. Real images & 102.210 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Quantitative evaluation result of the proposed method. Figure 6: Experimental environment: (**a**) virtual vessel-simulator, and (**b**) virtual camera sensor data. ### Horizon Targeted Loss Effects

The result of applying horizon-targeted loss is revealed in Figure 9. Notably, in the case of MUNIT [22], the line of the horizon is curved into a wave shape; this is not the case with the proposed method. ### Comparison of Object Detection Performance

After converting the virtual data to real-world data, three types of data were available: (1) real-world data, (2) virtual data, and (3) realistic data. We trained the YOLO [23] network, which is a deep learning algorithm for object detection in images, on real-data images to detect the positions of ships and their bounding boxes. We subsequently tested the network on the three types of data and compared the object-detection accuracies observed, as illustrated in Figure 10. The dimensions of the input image were 512 \(\times\) 910.