# ST-YOLOv8: Small-Target Ship Detection in SAR Images Targeting Specific Marine Environments
## 3 Experiment and Results
### Results and Discussion
#### 3.3.2 Comparison with Other Methods  
This section presents comparative experiments using five popular ship detection models: Faster R-CNN, SSD, DAPN, Quad-FPN, Retina Net, CTF-Net, and YOLOv5 on the SSDD and SSDv0, alongside the proposed ST-YOLOv8 model. Faster R-CNN improves the R-CNN series algorithms by introducing the Region Proposal Network (RPN) to reduce the computational cost of candidate regions and improve detection speed. SSD is a single-shot detection algorithm that performs target detection on feature maps of different scales, capable of detecting objects of varying sizes. DAPN enhances the model's ability to perceive important features by introducing channel attention and spatial attention mechanisms, improving detection accuracy, and is used for image recognition and target detection. Quad-FPN is a variant of the Feature Pyramid Network (FPN) used for object detection tasks. It enhances the model's ability to detect multi-scale targets by constructing a four-layer feature pyramid to fuse features at different scales. It boosts the model's ability to detect multi-scale targets by constructing a four-layer feature pyramid to fuse features at different scales. Focal loss helps the model focus more on hard-to-classify samples during the training process. These algorithms have their own advantages and application scenarios in the field of object detection, using different mechanisms to improve detection accuracy and speed. The experimental results show that ST-YOLOv8 achieved the best mAP (95.60%) on SSDD and 94.3% on SSDv0.  
This is attributed to the reallocation of the model's relevant parameters, elimination of irrelevant information, reduction in false alarm rates for targets such as islands, improve detection accuracy and speed. The proposed proposed method is based on the proposed proposed method. Faster R-CNN is a multi-scale target detection algorithm that performs target detection on feature maps of different scales. Faster R-CNN is a multi-scale target detection algorithm that performs target detection on feature maps of different scales.  
ment in recall, and enhanced sensitivity to medium and small ships. These adjustments enable the model to effectively extract discriminative features for detecting multi-scale ships in complex backgrounds. Table 7 presents the actual performance of different models on the SSDD and SSDv0. Table 8 provides a detailed comparison of our proposed model with other state-of-the-art models in terms of model size, number of parameters, computational speed, and floating-point operations. As shown in the table, our model achieves the smallest model size and the fewest number of parameters among all the compared models except YOLOv5n. However, the detection capability of YOLOv5n in Table 7 is not satisfactory. Our model can better demonstrate that it still performs relatively well under the condition of a compact structure, thereby highlighting its superior efficiency. This significant balance between model complexity and computational performance highlights the outstanding capabilities of the model we proposed, positioning it as a highly effective solution compared to other models.