# ST-YOLOv8: Small-Target Ship Detection in SAR Images Targeting Specific Marine Environments
## 3 Experiment and Results  
### Experiment Environment and Datasets  
All experiments were conducted on a Windows 11 Pro system using PyTorch 1.7.1 with CUDA 11.0 acceleration, equipped with an Intel i5-12400F processor, 16 GB RAM, and an RTX 3060 GPU. Models were trained using SGD optimization with Nesterov momentum (0.937), L2 weight regularization (5 \(\times\) 10\({}^{-4}\)), batch size 32, and initial learning rate 0.01 across 100-300 epochs. Overfitting mitigation included data augmentation, learning rate decay, and early stopping with 15-epoch patience, with hyperparameters refined through iterative validation.  
To validate the reliable performance of the model across various datasets, experiments were conducted using the SSDD and the SSDv0 to evaluate the proposed method. In addition to typical open-sea ship views, these two datasets include a variety of scenarios such as nearshore areas, ports, and islands.  
To enhance the dataset's complexity and robustness, a series of advanced techniques were implemented. Specifically, data augmentation strategies including random scaling (0.8\(\times\)-1.2\(\times\)) and random cropping (85-100% area) were employed to expand the diversity of the SSDD, enriching the range of scenarios and features available for model training. Furthermore, cross-dataset validation was performed by evaluating model performance across both SSDD and SSDv0 datasets, providing a comprehensive assessment of generalization capabilities across different data distributions. Complementary subset analysis was also conducted to examine model behavior under varied operational conditions, offering granular insights into performance characteristics across specific data partitions.  
To further examine the robustness of the models, subsets of the SSDD and SSDv datasets were created to simulate diverse environmental conditions. These included variations in weather conditions, sea states, and target densities, which allowed for a thorough assessment of the models' ability to perform reliably under different real-world scenarios. Through these meticulous approaches, a more comprehensive and reliable evaluation framework was established, ensuring that the models could effectively handle the inherent complexity and variability of the datasets. A detailed introduction to SSDD and SSDv0 follows below. Some of the detailed values are presented in Table 2. The annotation boxes in the dataset are all directly provided by the dataset.  
The SSDD comprises 1160 SAR images of varying sizes, sourced from three satellite sensors. The image resolution spans from 1 million to 15 million pixels. The dataset encompasses diverse imaging scenarios, including complex environments such as docks and nearshore areas, as well as simpler open-sea scenes. Each SAR image contains a variable number of ship targets, with differing sizes and types. Specifically, small, medium, and large ships constitute 60.2%, 36.8%, and 3% of the total vessels, respectively.  
The SSDv0 dataset is primarily constructed using SAR data from China's Gaofen-3 and Sentinel-1 satellites. Specifically, it consists of 102 scenes from Gaofen-3 and 108 scenes from Sentinel-1 SAR images, forming a high-resolution SAR ship target deep learning sample library. The current library contains 43,819 ship patches. The imaging modes of Gaofen-3 include Strip-Map (UFS), Fine Strip-Map 1 (FSI), Full Polarization 1 (QPSI), Full Polarization 2 (QPSII), and Fine Strip-Map 2 (FSII), with resolutions of 3 m, 5 m, 8 m, 25 m, and 10 m, respectively. Sentinel-1 SAR data, on the other hand, are acquired in Strip Mode (S3 and S6) and Wide Swath Mode. These diverse imaging modes and resolutions provide a rich source of data for deep learning applications in ship detection.