## 1 Introduction  
Recent technological advances in artificial intelligence (AI) have led to improvements in the field of autonomous driving. Autonomous vehicles learn diverse scenarios using open-source simulators, such as AirSim [1], CARLAR [2], and SVL Simulator [3], by training terrain and weather condition variables to respond in a manner that reflects real driving environments. The trained AI model is subsequently mounted on a real vehicle. Research similar to that of autonomous vehicles is now underway with respect to marine vessels, considering the effect of waves, buoyancy, water currents, and wind currents. The automation of navigation necessitates a robot to control the rudder and sails, steering the sailing yacht, making tactical decisions regarding the sailing routes, and performing docking maneuvers in ports. Virtual simulators of autonomous vessels, such as Freefloating Gazebos [4], VREP [5], RobotX Simulator [6], and USVSim [7], when used to simulate rudder adjustments according to the wind and tide, face the problem of a very low level of representativeness of marine graphics. Consequently, the performance of AI models trained to perform vision-based object tracking or pathfinding in a virtual ocean environment deteriorates when mounted in a real environment, owing to the differencesbetween the simulated and real environments. Changing the environment that is already built into a new style is limited because it requires manual effort. Therefore, we propose a diverse realistic marine image generation method that uses virtual images. Figure 1 depicts the conversion of image data obtained from a virtual simulator to provide realistic images pertaining to a variety of marine environments. Furthermore, this study generates realistic data that can train an artificially intelligent and vision-based autonomous vessel model by enhancing the virtual images of various styles obtained through the virtual autonomous vessel simulator, which is suitable for a photo-real world. Existing virtual simulators for autonomous vehicles have achieved good performance in transforming data from virtual images to those in realistic images through the generative adversarial network (GAN) [8], such as the use of virtual environment images obtained in the GTA game simulator and real-world driving data from Cityscapes [9]. However, when AI model architectures developed using images from land-based data are applied to marine images, the structural shapes within the images often collapse. On land, the locations and orientations of roads and vehicles are uniform; therefore, land-based images are nearly identical in structure; however, in the case of the ocean, the locations and orientations of the vessels differ widely owing to the absence of lanes. In addition, it is easy for neural networks to extract and learn features concerning land-based images because the color differences between elements, such as the sky, road, and vehicle, are evident. However, it is difficult to differentiate between the colors of the sky and sea in ocean-based images because they may comprise a range of similar blue colors, such as in the images depicted in Figure 2.  
Figure 1: Concept of realistic generation of diverse marine images for training autonomous vessels. Figure 2: Examples of marine data wherein image-feature extraction is difficult; (**a**) example of high structural diversity, and (**b**) example of low color difference. To overcome these limitations, we propose a method for the realistic generation of diverse marine images based on horizon-targeted loss that can preserve the shapes of the horizon and the vessel. Horizon-targeted loss calculates the difference in structural forms in the input and output images in the detected vessel areas based on the horizon and reflects them in the loss function, thereby enabling the network to prevent loss of the structural form of the relatively complex horizon and vessel.