###### Abstract  
Large amounts of high-quality image data are the basis and premise of the high accuracy detection of objects in the field of convolutional neural networks (CNN). It is challenging to collect various high-quality ship image data based on the marine environment. A novel method based on CNN is proposed to generate a large number of high-quality ship images to address this. We obtained ship images with different perspectives and different sizes by adjusting the ships' postures and sizes in three-dimensional (3D) simulation software, then 3D ship data were transformed into 2D ship image according to the principle of pinhole imaging. We selected specific experimental scenes as background images, and the target ships of the 2D ship images were superimposed onto the background images to generate \"Simulation-Real\" ship images (named SRS images hereafter). Additionally, an image annotation method based on SRS images was designed. Finally, the target detection algorithm based on CNN was used to train and test the generated SRS images. The proposed method is suitable for generating a large number of high-quality ship image samples and annotation data of corresponding ship images quickly to significantly improve the accuracy of ship detection. The annotation method proposed is superior to the annotation methods that label images with the image annotation software of Label-me and Label-img in terms of labeling the SRS images. SRS images; data augmentation; convolutional neural networks; target detection; image annotation +
Footnote †: journal: Medical  
method is to obtain published ship image data sets. The third method is to design simulation ship images with high similarity to the real ship images based on the existing real ship images. However, there are some problems in the ship images obtained by these three methods. The main problems of the ship images obtained by the camera are as follows: firstly, maritime scene are usually cover a vast area; thus, it is difficult to obtain high-definition images of target ships with ordinary surveillance cameras. Secondly, there are usually three types of equipment for acquiring images of ships inmaritime scenarios: passive cameras, active (PTZ) cameras, and drones. Since the passive camera and the active camera take fixed positions, the number of postures of the ship in the images acquired by these two devices is relatively limited. UAVs can obtain ship images of various postures of ships. However, the limitation of the power supply also brings great difficulties to the operation. In ports or docks with a high density of ships, drones cannot capture images at will due to the privacy of ship transportation; in some scenarios where ships are sparse, the number of ship types available is limited. The ship image data collected from the public data set also have the following problems: the primary public ship image dataset inludes: (1) MS-COCO [17]; (2) Open Images [18]; (3) ImageNet [19; 20]; (4) Pascal VOC [21]; (5) MarDCT [22]. There are lots of ship images in the above data sets. It is usually difficult to accurately classify ships in specific scenes using the ship image samples in the above single data set for training. The main constraint factors are summarized as follows: (1) ship types, (2) types of ship postures, (3)types of ship size, and (4) category of the background image. In addition, ship types are constantly updated according to the developments of the times, bringing difficulties to image classification and detection. There are some problems with the existing image generation methods for simulation ships. Data augmentation methods are often used to create simulation datasets. [PERSON] et al. [23] proposed a scheme that automatically selects the data augmentation manner. [PERSON] et al. [24] proposed a simple parameterization for targeting augmentation to the particular model and dataset sizes. [PERSON] et al. [25] proposed a color augmentation strategy for image data. [PERSON] et al.