# ST-YOLOv8: Small-Target Ship Detection in SAR Images Targeting Specific Marine Environments
## 5 Conclusions  
Maritime SAR images contain ships of various sizes, but existing detection algorithms face challenges in identifying ships of different sizes, particularly smaller vessels in complex scenes. To address this, the ST-YOLOv8 model, which is based on YOLOv8n, is proposed and integrates the C_OREPA module. At the junction of the backbone and neck networks, the SA mechanism and ASPP are incorporated into the model. Spatial and channel attention are combined through the inclusion of the SA mechanism, enhancing accuracy while maintaining low computational cost, and detection performance is significantly improved. The receptive field is expanded by ASPP through global pooling and large dilation rate convolutional kernels. Feature maps from different levels are effectively fused by the model, further improving the detection rate of small vessels in complex scenes. Finally, the W-IoU loss function has been introduced to enhance the recognition of multi-scale ships by focusing on regions containing ship information. Detection accuracy is improved, and background interference is reduced. To improve the detection accuracy of small targets and reduce dependence on environmental variables, the highest detection accuracy and strong generalization capability are demonstrated by ST-YOLOv8 in comparisons on the SSDv0 and SSDD. The low recognition accuracy in complex environments is primarily caused by detection errors and missed detections. Compared to YOLOv8n's accuracy on the SSDD, ST-YOLO improves it from 89% to 94.1%, the recall rate from 76.7% to 82%, and F1 score from 82.3% to 87.6%. On the SSDv0, accuracy increased from 83.3% to 92.7%, recall rate from 76% to 84.5%, and F1 score from 79.4% to 88.1%. The best results are achieved by the ST-YOLOv8 model compared with other popular ship detection algorithms such as Faster R-CNN, SSD, DAPN, Quad-FPN, and Retina Net, with an accuracy of 94.1% and an mAP of 95.6% on the SSDD, and an accuracy of 92.7% and an mAP of 94.3% on the SSDv0. Multi-scale ships in SAR images are effectively detected by ST-YOLOv8, making it crucial for defense and civilian search and rescue missions. In search and rescue missions, the enhancement of model accuracy and the reduction in false alarm rates play a crucial role in improving response times, monitoring illegal activities, and optimizing port management. Specifically, higher model accuracy enables more precise target identification, thereby significantly reducing the time wasted on ineffective searches and shortening the overall response time. Moreover, a lower false alarm rate allows surveillance systems to more accurately detect illegal activities, minimizing unnecessary alerts and resource allocation, and thereby enhancing the efficiency of monitoring. Additionally, in port management, high-precision models can effectively track ship dynamics in real-time, optimize the allocation of port resources, and reduce congestion, ultimately improving overall operational efficiency. These improvements not only enhance the efficiency of mission execution but also provide robust support for safety management in relevant fields.  
Data curation, Y.T., Y.W. and Y.Z.; Investigation, Y.W. and Y.Z.; Methodology, F.G. and Y.T.; Validation, Y.T., Y.W. and Y.Z.; Writing--original draft, F.G. and Y.T.; Writing--review and editing, F.G. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Natural Science Foundation of Hebei Province (No. F2024210005, No. F2022210023).  
Not applicable.  
Data Availability Statement: The data that support the findings of this study are available from the authors, upon reasonable request.  
The authors declare no conflicts of interest.