## 4 Results
### Comparative Experiment  
Table 3 shows the comparison of the application of several models to the SSDD dataset. When applied to the 30% SSDD dataset, our model performed well in terms of both accuracy and AP: 13.35% higher than the SSD model, 12.1% higher than the YOLO v5, and 20.27% higher than the FPN model. In addition, our model performed the best in terms of the average detection rate AP, which was 92.92%. Our model still performed well when using 30% of the other datasets. On the 30% OpenSARShip dataset, the model accuracy rate was 91.72%, and the recall rate was 90.46%, which was slightly lower than that of the SSDD dataset. On the 30% HRSID dataset, the model performance was slightly worse, but it was more than 10% higher than that of the other models. This proved that our model was more suitable for ship target detection in SAR images.  
Table 2 shows that the ship target detection model in this paper had the highest accuracy on the SSDD dataset. At the same time, the two indicators of recall and AP were also higher in each model. In regard to the OpenSARShip dataset, our model had the highest recall and AP. When applied to the HRSID dataset, the model effect was poor. We also used the single shot multibox detector (SSD) model, YOLO v5 model, and feature pyramid network (FPN) model on the SSDD dataset, and the effect was proven to be lower than that of the ship object detection model in this paper. Therefore, the model in this paper exhibited better performance and was better adapted to target detection than other models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Networks** & **Dataset Size (\%)** & **Precision (\%)** & **Recall (\%)** & **mAP (\%)** \\ \hline Attention only & 30 & 89.58 & 88.27 & 89.31 \\ Attention (one layer) + data augmentation & 30 & 90.73 & 90.59 & 90.87 \\ Attention (two layer) + data augmentation & 30 & 88.47 & 89.15 & 89.94 \\ Attention (one layer) + loss & 30 & 92.37 & 92.72 & 93.25 \\ Attention (two layer) + loss & 30 & 91.78 & 91.25 & 92.53 \\ Attention (one layer) + data augmentation + loss & 30 & 91.41 & 92.64 & 93.85 \\ Attention (two layer) + data augmentation + loss & 30 & 91.15 & 90.79 & 92.81 \\ Attention only & 100 & 90.91 & 89.84 & 92.89 \\ Attention (one layer) + data augmentation & 100 & 92.27 & 91.02 & 93.35 \\ Attention (two layer) + data augmentation & 100 & 91.62 & 92.24 & 93.10 \\ Attention (one layer) + loss & 100 & 90.45 & 93.83 & 93.79 \\ Attention (two layer) + loss & 100 & 92.76 & 92.58 & 93.52 \\ Attention (one layer) + data augmentation + loss & 100 & 93.84 & 92.81 & 94.27 \\ Attention (two layer) + data augmentation + loss & 100 & 92.59 & 91.21 & 93.96 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation experiment.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Networks** & **Dataset Size (\%)** & **Precision (\%)** & **Recall (\%)** & **mAP (\%)** \\ \hline SSD + SSDD & 30 & 77.23 & 78.16 & 79.57 \\ YOLO v5 + SSDD & 30 & 78.68 & 78.58 & 80.82 \\ Our model + SSDD & 30 & 90.17 & 91.14 & 92.92 \\ FPN + SSDD & 30 & 71.53 & 72.40 & 72.65 \\ Our model + OpenSARShip & 30 & 91.72 & 90.46 & 92.13 \\ Our model + HRSID & 30 & 91.51 & 90.48 & 91.96 \\ SSD + SSDD & 100 & 96.26 & 96.17 & 96.51 \\ YOLO v5 + SSDD & 100 & 96.53 & 96.38 & 96.60 \\ Our model + SSDD & 100 & 94.02 & 92.62 & 95.88 \\ FPN + SSDD & 100 & 94.46 & 93.20 & 95.93 \\ Our model + OpenSARShip & 100 & 92.67 & 93.84 & 94.95 \\ Our model + HRSID & 100 & 92.59 & 93.43 & 94.72 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative experiment.  
When using the 100% SSDD dataset, our model was comparable to other models in terms of both accuracy and AP, which remained at 95.88%. Our model performed well on the other two datasets, reaching slightly higher levels than the other models.