## 1 Introduction  
Synthetic aperture radar (SAR) [1] images are a promising data source for ocean activity detection. They are widely used in maritime trade and vessel activity and monitoring. SAR images provide all-weather and all-day imaging information and are not affected by meteorological conditions. Different from optical remote sensing images and multispectral images, radar images with higher resolution can still be obtained in conditions with interference, such as clouds and dense fog, which are more suitable for marine climates. Currently, using SAR images as data sources is an important method for ship target detection [2]. SAR image data are precious and not readily available. However, in the field of remote sensing detection, compared with other data sources, SAR images have many unique characteristics as data sources, which are described as follows:  
1. With the advancement of observation technology, several high-resolution SAR imaging satellites have been put into use, and the amount of SAR data has continued to increase. However, compared with conventional optical images, SAR images are more difficult to interpret, their features are not intuitive, and various coherent speckles and overlapping phenomena increase the difficulty of target interpretation. It is very difficult to obtain accurate and large-scale SAR data sources, and many experienced researchers spend considerable time doing so. The existing database of public SAR images of ship targets is very valuable [3]. Many scholars have investigated how to use the existing data to train a detector with better performance and stronger adaptability. Reasonable data augmentation before training the model is an important means to improve the convergence speed of the model and improve the detection efficiency. In this paper, we carry out targeted, scientific and practical data enhancement according to the characteristics of SAR ship targets; this is used to increase the proportion of positive sample data and to help design detectors with better performance. 2. The ship target detection algorithm of SAR images is suitable for real-world applications with limited data because it reduces data dependence. In the field of deep learning, a common method to improve the model detection effect is to increase the amount of data for training the model. In recent years, to promote research on SAR images in ship target detection, domestic and foreign research institutions have successively released multiple data sources, such as the Sentinel-1 Ship Interpretation Dataset (OpenSARShip) [4; 5]; SAR Ship Detection Datasets (SSDD/SSDD+) [6; 7; 8; 9; 10; 11; 12]; High-Resolution SAR Ship Objection Detection Dataset version (AIR-SARShip2.0) [13]; SAR-Ship-Dataset [14]; and High-Resolution SAR Image Dataset (HRSID) [15]. These publicly available data effectively alleviate the difficulty of ordinary researchers in obtaining data. Consequently, with so many public datasets, there are more than 27,400 images available. However, this is a small number compared to the common deep learning datasets that often include hundreds of thousands or tens of millions of images. Therefore, when detecting ships in SAR images, researchers are concerned with finding ways to use less data to obtain better detection results. The original goal of this paper was not to design a detector with a better detection rate or higher accuracy but to develop a method for using less data to obtain the same or similar detection results. To reduce the data dependence of the model, 30% of the data in the SSDD training dataset were used for comparative experimental research. Model uses separately trained with the complete training set and trained with 30% of the training set. The two models trained under different conditions were compared under the same tuning condition, obtain the average value of multiple experimental tests. Our goal is that the final designed model can achieve similar or close detection accuracy to the model trained using the full training dataset when using less training data.