# 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
For multi-scale SAR images of docked ships with complex backgrounds (e.g., medium-sized ships near the shore), Figure 8e reveals that YOLOv8n not only misses ships at the image boundary but also generates false positives by misclassifying coastal structures as vessels (marked in yellow). This error stems from the model's susceptibility to interference from cluttered dock backgrounds. Compared to the YOLOv8n model, the proposed model incorporates SA, enabling it to focus more on ship features and ignore irrelevant background information. Therefore, this model has higher recognition accuracy when facing the pier background. In the SAR images of densely arranged multi-scale ships, the experimental results indicate that the YOLOv8n model exhibits more severe misdetection, represented by yellow circles, when it encounters ships closer to the shore. Figure 7: Comparison of loss function indicators between ST-YOLOv8 and YOLOv8n: (**a**) box loss during training; (**b**) classification loss during training; (**c**) distribution focal loss during training; (**d**) precision; (**e**) recall; (**f**) when the IoU threshold is 0.5, the average accuracy of the model; (**g**) the average accuracy of the model within the IoU threshold range of 0.5 to 0.95; (**h**) box loss during validation; (**i**) classification loss during validation; (**j**) distribution focal loss during validation. The ST-YOLOv8 model demonstrates better performance when dealing with densely parked multi-scale ships, as the model combines depthwise separable convolutions with different dilation rates and atrous convolutions, which collectively improve its multi-scale feature extraction capability. Even when faced with overlapping small targets, the model maintains good recognition performance. In the validation experiments on the SSDv0 dataset, as illustrated in Figure 9, a visual analysis was conducted on 16 representative image tiles (average size: 256 \(\times\) 256 pixels) containing densely distributed small ship targets (ranging from 8 \(\times\) 8 to 32 \(\times\) 32 pixels). The ST-YOLO model successfully detected all small ship targets across these tiles, identifying a total of 26 valid objects (with all targets in each image being correctly recognized), significantly reducing the missed detection rate compared to the baseline model. The Intersection over Union (IoU) values predominantly exceeded 0.6. Notably, in the complex sea clutter background of the second tile from the left in the fourth row, ST-YOLO effectively distinguished a blurred ship target measuring 16 \(\times\) 20 pixels (confidence score: 0.4), demonstrating the efficacy of the proposed multi-scale feature enhancement module in extracting small target features. Quantitative analysis revealed that this set of tiles achieved the highest average precision, further highlighting the superior performance of ST-YOLO in small ship target detection tasks. Figure 8: Comparison of detection results: (**a**) real and accurate box for small and medium-sized targets in the sea area (green box); (**b**) performance of YOLOv8n baseline model in the state of small targets in large sea areas; (**c**) performance of ST-YOLOv8 model in the state of small targets in large sea areas; (**d**) the true position of a ship in complex background situations such as docking and port (green box); (**e**) performance of YOLOv8n baseline model in complex backgrounds; (**f**) performance of ST-YOLOv8 model in complex backgrounds.