# Syn2 Real Detection in the Sky: Generation and Adaptation of Synthetic Aerial Ship Images
## 1 Introduction  
In the past few decades, deep learning has made significant advancements across various research fields. In computer vision, deep learning-based methods have gained widespread adoption in scientific research and industrial applications, particularly in the domain of object detection. The performance of object detection methods heavily relies on the availability of large and diverse datasets. Several prominent examples of such datasets include ImageNet [1], which is utilized by the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), and VOC2007 and VOC2012 [2], which were released by the PASCAL Visual Object Classes (PASCAL VOC). Additionally, there are other notable datasets like Microsoft Common Objects in Context (COCO) [3] and Google Open Images [4]. These datasets encompass a vast number of images, object instances, and categories, thereby enhancing the effectiveness of training, validation, and testing processes for object detection models. In recent years, remote sensing images have found widespread applications in various fields, such as geographic information systems, environmental monitoring, and urban planning. The detection and analysis of remote sensing images rely heavily on the availability of large datasets. However, acquiring real remote sensing images poses numerous challenges due to signal interference, geographical conditions, lighting variations, dust, and other factors. The annotation process for these images is complex, time-consuming, and expensive. These factors impede the progress and practicality of various remote sensingimage processing tasks. To address these challenges, researchers have utilized several well-established remote sensing image datasets, including DOTA [5], UCAS-AOD [6], NWPU VHR-10 [7], DIOR [8], and TGRS-HRRSD-Dataset [9]. However, these datasets are limited in scale and struggle to meet the growing demands of remote sensing research. To overcome these limitations, the creation of synthetic remote sensing image datasets has emerged as an effective solution. In this study, we focused on investigating object detection using commonly found ships in aerial remote sensing image datasets. The synthesized image datasets were specifically generated with ships as the exclusive object. Synthetic remote sensing image datasets comprise a series of images created by simulating the visual characteristics and geometric features of real-world scenes. These synthetic images can be generated using real remote sensing images as a basis or can be entirely controlled by the textual descriptions of a scene's main content. By incorporating variations in factors such as lighting conditions, weather conditions, and seasonal changes, a diverse range of remote sensing images can be simulated. Some research studies have demonstrated the feasibility of synthetic image datasets. For example, Meta's realistic synthetic dataset was developed using Unreal Engine [10], Cam2 BEV [11], and ProcSy [12] for synthesizing road scene images in the field of autonomous driving. Furthermore, game engine-based synthetic datasets have been established for tasks such as object recognition and video semantic segmentation. Establishing synthetic remote sensing image datasets offers several significant advantages. Firstly, the generation process of synthetic images is controllable, allowing for the flexible adjustment of parameters and variation factors to produce synthetic images tailored to specific scenarios. This capability enables researchers to conduct large-scale experiments and tests under different conditions, thereby enhancing the robustness and reliability of algorithms. Secondly, synthetic remote sensing image datasets can provide rich label information, which is crucial for deep learning tasks. By introducing labels such as land cover categories, bounding boxes, and semantic segmentation during the generation process, accurate references can be provided for algorithm training and evaluation.