# Syn2 Real Detection in the Sky: Generation and Adaptation of Synthetic Aerial Ship Images
## 2 Related Works
### Synthetic Image Datasets  
Due to the time-consuming, labor-intensive, and costly nature of acquiring real images to construct a dataset, the synthesis of images has gained popularity as an alternative approach. Synthetic images can be generated through two main pathways: physics engine-based synthesis methods and computer vision algorithm-based synthesis methods. Physics \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Type** & **Name** & **Images** & **Categories** & **Instances** & **Year** \\ \hline \multirow{4}{*}{General Dataset} & ImageNet [1] & 14,197,122 & 21,841 & / & 2009 \\  & VOC [2] & 33,043 & 20 & 79,540 & 2007+2012 \\  & COCO [3] & 330 K & 80 & 1.5 M & 2014 \\  & Open Images V7 [4] & 1.9 M & 600 & 15.4 M & 2022 \\ \hline \multirow{8}{*}{Aerial Dataset} & NWPU VHR-10 [7] & 800 & 10 & / & 2014 \\  & UCAS-AOD [6] & 910 & 2 & 6029 & 2014 \\  & VEDAI [20] & 1210 & 9 & 3640 & 2015 \\  & HRSC2016 [22] & 1070 & 1 & 2976 & 2016 \\  & COWC [23] & 53 & 4 & 32,716 & 2016 \\  & SSDD [24] & 1160 & 1 & 2358 & 2017 \\  & RSOD-Dataset [25] & 976 & 4 & 6950 & 2017 \\  & airbus-ship-detection [26] & 42,556 & 1 & 81,011 & 2018 \\  & DIOR [8] & 23,463 & 20 & 192,472 & 2018 \\  & xView [27] & 1413 & 60 & 800,636 & 2018 \\  & DOTA [5] & 2806 & 15 & 188,282 & 2018 \\  & ITCVD [28] & 173 & 1 & 29,088 & 2018 \\  & LEVIR [21] & 21,952 & 3 & 10,467 & 2018 \\  & TGRS-HRRSD-Dataset [9] & 21,761 & 13 & 55,740 & 2019 \\  & AIR-SARSip-1.0 [29] & 31 & 1 & 461 & 2019 \\  & HRSID [30] & 5604 & 1 & 16,951 & 2020 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A comparison between general and aerial datasets in terms of the number of images, categories, and instances. engine-based methods utilize platforms like Unity3D or the Unreal Engine. Computer vision algorithm-based methods encompass rule-based approaches, generative adversarial network (GAN)-based [31] methods, variant autoencoder (VAE)-based [32] methods, and diffusion model [33; 34] methods. Each technique has its own advantages and disadvantages. Rule-based generative models offer faster sample generation but come with higher computational complexity and numerous constraints and limitations. Generative adversarial network-based methods generate images with high fidelity but may suffer from issues such as a lack of diversity. Variational autoencoder-based generated images tend to be smoother but offer less controllability. Diffusion model-based images are of higher quality but require more iterations; furthermore, they are computationally expensive, and adjusting their parameters can be challenging. Several synthetic image datasets have been widely and successfully utilized in various fields. Meta has developed a photorealistic synthetic dataset called Photorealistic Unreal Graphic (PUG) using Unreal Engine 5, which offers improved control over AI vision systems and enables robust evaluation and training [10]. Stable Diffusion has been used to generate facial datasets, including the first and largest artificial emotion dataset [35; 36]. In the field of autonomous driving, synthetic datasets like Cam2 BEV and ProcSy provide dynamic road scene images and aid in pedestrian recognition [11; 12]. Virtual KITTI is another synthetic dataset used for comparison with real datasets in autonomous driving research [37]. For object recognition and segmentation, synthetic datasets built on game scenarios and physics engines include PersonX, SAIL-VOS, and GCC [38; 39; 40]. DiffusionDB is a dataset containing 2 million images with textual descriptions of the generated images [41]. There are even game engines that specialize in generating synthetic images. G2D captures video from GTA V [42]. CARLA supports the entire process of an autopilot system [43]. MINOS simulates various house scenes [44]. Therefore, it can be concluded that synthetic aerial image datasets have the potential to be applied in object detection tasks on aerial images. In fact, there have been several attempts in this direction. [PERSON] et al. introduced a multi-modal synthetic dataset for this purpose [45].