## 3 Proposed Diverse and Realistic Marine Image Generation Framework
### Discriminator
We trained the YOLO [23] network using 500 real training data images. Subsequently, we tested 200 images each of real, virtual, and realistic data. Figure 7: Results of the proposed generator: (**a**) results of image translation from the virtual to real domain, and (**b**) results of image translation from the real to virtual domain. Table 4, which lists the object detection accuracies obtained for the three types of data, reveals that the object detection accuracy is higher on testing within the case of the realistic data than that obtained within the case of the virtual data. This indicates that we generated realistic images that resembled the real data more closely. \begin{table}
\begin{tabular}{c c} \hline
**Data Type** & **Object Detection Accuracy** \\ \hline Real data & 85\% \\ \hline Virtual data & 79\% \\ \hline Realistic data & 81\% \\ \hline \end{tabular}
\end{table} 
Table 4: Comparison results of object detection performance. Figure 8: Compared results of four different style image mapping. Figure 10: Ship detection results using three different data types: real, virtual and realistic. Figure 9: Horizon-targeted loss effect results; the first column shows that MUNIT [22] and our proposed method can generate realistic images which do not include the horizon line. However, the second and the third column show our proposed method maintain the shape of horizon line in a yellow box and vessel shape in a green box more clearly than MUNIT [22] in the case of horizon scene. ### Inference Time

The proposed method, which applies horizon-targeted loss, does not require semantic information from the input. Because the algorithm requires considerable time to compare losses in the training stage, the training time increases, and no significant difference between MUNIT [22] and the proposed algorithm is observed after training. Therefore, unlike in MUNIT [22], no additional computation is required at the time of inference because the input image and network layers remain the same. Furthermore, the accuracy of image conversion was improved by reducing the error in identifying the horizon area. Table 5 shows that we achieved an inference time of 0.032 s (31.25 fps) with 640 \(\times\) 480-pixel images on a GeForce GTX 3090 Ti graphics card. ## 5 Conclusions

In this study, we propose a virtual-to-realistic marine image style transfer method using horizon-targeted loss for marine data that can preserve the shapes of the horizon and the vessel. Horizon-targeted loss focuses on distinguishing the horizon from the other structural forms in the input and output images, based on a comparison of the segmented shape. Experiments reveal that the proposed method preserves the structural shapes on the horizon more accurately than the existing algorithms. In addition, a higher object detection accuracy is observed on learning using the augmented learning data of various walkdown styles compared with learning using virtual data alone. The proposed method allows us to generate realistic data to train AI models of vision-based autonomous vessels by actualizing and augmenting virtual images acquired from virtual autonomous-vessel simulators. A comparison between the proposed and MUNIT [22] methods via visual assessment and quantitative analysis reveals that our method achieves better performance in maintaining the shapes of the horizon and vessels. In future work, we will modify the proposed model to obtain high-resolution images in real time. Conceptualization, methodology, and writing--original draft preparation, J.P.; software, T.H.C. ; project administration and funding acquisition, K.C. All authors have read and agreed to the published version of the manuscript. This research was supported by the Future Challenge Program through the Agency for Defense Development funded by the Defense Acquisition Program Administration. Not applicable. Not applicable. Not applicable. Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest.