# 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  
To systematically evaluate the contributions of individual components and their synergistic interactions within the proposed framework, comprehensive ablation experiments were conducted on two benchmark datasets: SSDD and SSDv0. The experimental protocol was divided into two complementary stages: Single-Module Impact Assessment and Progressive Module Integration Analysis. This two-tiered ablation strategy provides rigorous empirical evidence for both the standalone efficacy and collaborative interactions of the proposed architectural innovations. Firstly, in this paper, the SPPF module in YOLOv8n is replaced with the ASPP model. In the Ablation Experiment, Table 3 shows that ASPP significantly improves the performance of SAR ship target detection. This improvement is mainly because traditional \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **OPERA** & **SA** & **ASPP** & **W-IoU** & **P** & **R** & **F1** \\ \hline SSDv0 & & & & & 0.833 & 0.760 & 0.794 \\ SSDv0 & \(\surd\) & & & & 0.879 & 0.773 & 0.823 \\ SSDv0 & \(\surd\) & \(\surd\) & & & 0.895 & 0.794 & 0.841 \\ SSDv0 & \(\surd\) & \(\surd\) & \(\surd\) & & 0.906 & 0.851 & 0.861 \\ SSDv0 & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 0.927 & 0.845 & 0.881 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Ablation experiment progressive module integration analysis (SSDV0). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Dataset** & **P** & **R** & **F1** & **mAP** & \
\begin{tabular}{c} **Inference** \\ **Time (FPS)** \\ \end{tabular} &
\begin{tabular}{c} **Model Size** \\ **(MB)** \\ \end{tabular} \\ \hline Faster R-CNN & SSDD & 0.871 & 0.730 & 0.794 & 0.889 & 35.6 & 107.0 \\ SSD & SSDD & 0.862 & 0.750 & 0.802 & 0.892 & 38.6 & 90.6 \\ DAPN & SSDD & 0.856 & 0.766 & 0.808 & 0.850 & 48.4 & 82.1 \\ Quad-FPN & SSDD & 0.923 & 0.833 & 0.875 & 0.935 & 57.1 & 56.7 \\ RetinaNet & SSDD & 0.912 & 0.821 & 0.864 & 0.934 & 69.1 & 528.5 \\ CTF-Net & SSDD & 0.909 & 0.796 & 0.857 & 0.911 & 198.4 & 25.6 \\ YOLOv5n & SSDD & 0.858 & 0.721 & 0.783 & 0.841 & 318.4 & 3.9 \\ ST-YOLOv8(ours) & SSDD & 0.941 & 0.820 & 0.871 & 0.951 & 300.5 & 8.6 \\ Faster R-CNN & SSDv0 & 0.883 & 0.710 & 0.787 & 0.852 & 36.2 & 107.0 \\ SSD & SSDv0 & 0.897 & 0.745 & 0.797 & 0.864 & 37.4 & 90.6 \\ DAPN & SSDv0 & 0.863 & 0.773 & 0.815 & 0.843 & 44.6 & 82.1 \\ Quad-FPN & SSDv0 & 0.912 & 0.815 & 0.861 & 0.928 & 59.8 & 56.7 \\ RetinaNet & SSDv0 & 0.901 & 0.806 & 0.851 & 0.916 & 64.8 & 528.5 \\ CTF-Net & SSDv0 & 0.912 & 0.856 & 0.870 & 0.927 & 208.5 & 25.6 \\ YOLOv5n & SSDv0 & 0.867 & 0.786 & 0.824 & 0.853 & 320.8 & 3.9 \\ ST-YOLOv8(ours) & SSDv0 & 0.921 & 0.927 & 0.881 & 0.943 & 299.7 & 8.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Experimental results of different methods. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Model Size** & **Parameter** & **FLOPs** & **Speed** \\  & **(MB)** & **(M)** & **(G)** & **(fps)** \\ \hline Faster R-CNN & 107.0 & 27.6 & 459.0 & 36.2 \\ SSD & 90.6 & 23.6 & 87.4 & 37.4 \\ DAPN & 82.1 & 16.8 & 56.6 & 44.6 \\ Quad-FPN & 56.7 & 21.5 & 68.8 & 59.8 \\ RetinaNet & 528.5 & 145.2 & 190.6 & 64.8 \\ CTF-Net & 25.6 & 15.7 & 60.6 & 208.5 \\ YOLOv5n & 3.9 & 1.9 & 4.5 & 320.8 \\ ST-YOLOv8(ours) & 8.6 & 6.5 & 10.6 & 299.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance comparison of network models on SSDv0. downsampling increases the receptive field but reduces spatial resolution. In contrast, dilated convolution expands the receptive field while maintaining resolution. Different dilation rates provide the network with various receptive fields, enabling the model to capture multi-scale contextual information. Table 4 shows that adding the ASSP module improves the recall rate and precision to 93.1% and 80%, respectively, indicating that the SA module reduces missed detections caused by unknown feature extraction in the YOLOv8n algorithm.