# ST-YOLOv8: Small-Target Ship Detection in SAR Images Targeting Specific Marine Environments
## 3 Experiment and Results
### Results and Discussion
#### 3.3.1 Ablation Experiment
Tables 3 and 4 present a performance comparison of different improvement strategies on the SSDD, while Tables 5 and 6 focus on results from the SSDv0. These data include both quantitative metrics, such as accuracy and recall, and qualitative analyses, such as the model's performance in detecting specific object types. Through these detailed experiments and analyses, a scientific basis for further optimization of ST-YOLOv8 was provided, and directions for future research were identified. Specifically, Tables 3 and 5 illustrate the performance changes when individual new modules are incorporated into the base model. This approach allows us to clearly observe the distinct contributions of each module to the overall model performance. Tables 4 and 6, on the other hand, demonstrate the process of incrementally integrating these modules into the complete model. Through comparative analysis, these tables highlight the cumulative enhancement of model performance achieved by the combined integration of these modules. Finally, the application of the W-IoU loss function introduces a dynamic non-monotonic focusing mechanism and a focusing coefficient. This enables the model to handle small targets more precisely and accurately, thereby improving performance in object detection tasks. The loss function is considered a fundamental component in object detection tasks, as it quantifies the discrepancy between the predicted and true values, thereby directly influencing the overall performance of the model. Among the various loss components, including box loss, classification loss, and distribution focal loss, superior performance is demonstrated by ST-YOLOv8, primarily due to the incorporation of W-IoU. This enhancement results in more accurate bounding box predictions, which improve both localization and classification precision. When a comparison is made with YOLOv8n, significant improvements are achieved by ST-YOLOv8 across three key evaluation metrics: precision, recall, and mAP. Specifically, detection accuracy is enhanced, and recall is improved, ensuring that fewer objects are missed during detection. Additionally, the increase in mean average precision reflects a more robust model that excels in both the quality and quantity of detections. The marked improvements in these metrics are clearly illustrated in Figure 7, demonstrating the effectiveness of the proposed enhancements and highlighting the significant performance gains of ST-YOLOv8 over its predecessor, YOLOv8n. The importance of the W-IoU modification in driving improved detection capabilities across diverse object categories and detection scenarios is underscored by this comparative advantage. The proposed methodology demonstrates enhanced comprehensiveness and integration, rendering it particularly advantageous for the training process. As illustrated in the comparative analyses Figure 8, ST-YOLOv8 exhibits superior performance relative to YOLOv8n across all evaluated metrics. Figure 8 presents the detection performance of both models when applied to diverse SAR ship imagery datasets. Notably, under complex environmental conditions, ST-YOLOv8 demonstrates significantly improved capability in detecting multi-scale ship targets compared to the baseline YOLOv8n architecture. This study further validates the effectiveness of the proposed algorithm for SSDv0 detection, with results illustrated in Figure 8. Among them, the first row is for small targets in large sea areas, and the second row is for small targets with complex backgrounds. The comparison demonstrates that ST-YOLOv8 achieves superior recognition accuracy. Figure 8a shows the true distribution of ships in the image. As shown in Figure 8b, YOLOv8n fails to detect small and medium-sized ships (highlighted by the blue circle), primarily because their limited pixel coverage leads to false negatives. In contrast, Figure 8c presents the results of our proposed ST-YOLOv8 model, which accurately identifies small ship targets with higher confidence.