# YOLO-SWD--An Improved Ship Recognition Algorithm for Feature Occlusion Scenarios
## 4 Results and Analysis
### Experimental Results
Our method achieves a Precision of 68.5%, Recall of 53.5%, and F1 of 60.1%, outperforming other versions, particularly in recall rate, indicating reduced miss rates. However, CVs remain a challenging category due to severe near-shore occlusion, small target sizes, and inherent model limitations in complex environments. Despite this, our model maintains balanced Precision and Recall, demonstrating advantages in minimizing both false alarms and misses. Figure 8: Qualitative comparison results (1–12) (magenta: CV, navy blue: DDG, white: CVN, light green: LST, light blue: CG; the orange and blue ellipses and red triangles represent missed detections and wrong detections, respectively). Comprehensive analysis reveals that from YOLOv8 to YOLO-SWD, all algorithms exhibit gradual optimization across most categories, particularly in large target categories (DDG and CVN), but significant gaps persist in small-target (CV) and low-sample (SP) categories. A detailed class-specific analysis follows:  
In the DDG category, CenterNet leads all models with 89.8% Precision, slightly outperforming our method (83.7%). Other models, such as YOLOv8 (81.8%) and YOLOv10 (81.5%), lag behind. In recall, YOLOv11 leads (88.5%), but our method balances Precision \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Category** & **Precision (\%)** & **Recall (\%)** & **F1** & **AP (\%)** & **MAP (\%)** \\ \hline \multirow{8}{*}{YOLOv8} & DDG & 81.8 & 86.6 & 84.1 & 89.6 & \multirow{8}{*}{80.5} \\  & CVN & 95.6 & 99.0 & 97.3 & 99.4 & \\  & CG & 76.9 & 88.9 & 82.5 & 91.1 & \\  & LST & 76.6 & 79.2 & 77.9 & 84.6 & \\  & CV & 68.5 & 45.9 & 54.9 & 55.2 & \\  & SP & 79.6 & 60.0 & 68.4 & 62.8 & \\ \hline \multirow{8}{*}{YOLOv9} & DDG & 84.2 & 83.4 & 83.8 & 89.9 & \multirow{8}{*}{80.1} \\  & CVN & 95.1 & 99.5 & 97.3 & 99.3 & \\ \cline{1-1}  & CG & 77.4 & 89.2 & 82.9 & 82.1 & \\ \cline{1-1}  & LST & 88.2 & 84.3 & 86.2 & 87.2 & \\ \cline{1-1}  & CV & 70.8 & 47.4 & 56.8 & 56.9 & \\ \cline{1-1}  & SP & 47.0 & 60.0 & 52.7 & 60.6 & \\ \hline \multirow{8}{*}{YOLOv10} & DDG & 81.5 & 85.0 & 83.2 & 88.8 & \multirow{8}{*}{80.1} \\  & CVN & 94.3 & 96.6 & 95.4 & 98.9 & \\ \cline{1-1}  & CG & 73.1 & 86.4 & 79.2 & 87.6 & \\ \cline{1-1}  & LST & 79.0 & 77.4 & 78.2 & 78.2 & \\ \cline{1-1}  & CV & 68.2 & 47.2 & 55.8 & 55.8 & \\ \cline{1-1}  & SP & 100 & 53.1 & 69.4 & 71.3 & \\ \hline \multirow{8}{*}{YOLOv11} & DDG & 80.6 & 88.5 & 84.4 & 90.5 & \multirow{8}{*}{81.2} \\  & CVN & 96.5 & 97.6 & 97.0 & 99.1 & \\ \cline{1-1}  & CG & 74.0 & 90.4 & 81.4 & 91.0 & \\ \cline{1-1}  & LST & 84.8 & 79.2 & 81.9 & 84.6 & \\ \cline{1-1}  & CV & 79.6 & 51.8 & 62.7 & 57.8 & \\ \cline{1-1}  & SP & 100 & 58.8 & 74.1 & 66.5 & \\ \hline \multirow{8}{*}{Faster-RCNN} & DDG & 75.7 & 76.6 & 76.1 & 80.6 & \multirow{8}{*}{71.0} \\  & CVN & 89.2 & 96.4 & 92.6 & 95.8 & \\ \cline{1-1}  & CG & 69.2 & 74.3 & 71.6 & 79.9 & \\ \cline{1-1}  & LST & 74.1 & 65.3 & 69.4 & 75.8 & \\ \cline{1-1}  & CV & 78.2 & 33.8 & 47.1 & 43.9 & \\ \cline{1-1}  & SP & 100 & 33.3 & 40.0 & 50.1 & \\ \hline \multirow{8}{*}{CenterNet} & DDG & 89.8 & 80.6 & 84.9 & 90.5 & \multirow{8}{*}{75.7} \\  & CVN & 96.4 & 94.5 & 95.4 & 97.3 & \\ \cline{1-1}  & CG & 80.2 & 73.2 & 76.5 & 81.9 & \\ \cline{1-1}  & LST & 77.8 & 68.8 & 73.0 & 69.2 & \\ \cline{1-1}  & CV & 73.7 & 51.3 & 60.4 & 56.7 & \\ \cline{1-1}  & SP & 100 & 25 & 49.9 & 58.4 & \\ \hline \multirow{8}{*}{YOLO-SWD} & DDG & 83.7 & 85.4 & 84.5 & 90.7 & \multirow{8}{*}{83.9} \\  & CVN & 95.4 & 98.6 & 96.9 & 99.3 & \\ \cline{1-1}  & CG & 74.7 & 90.9 & 82.0 & 91.9 & \\ \cline{1-1}  & LST & 81.9 & 79.2 & 80.5 & 85.3 & \\ \cline{1-1}  & CV & 68.5 & 53.5 & 60.1 & 58.1 & \\ \cline{1-1}  & SP & 100 & 71.7 & 83.5 & 78.1 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantitative comparison of experimental results. and Recall, achieving the highest AP (90.7%). In the CV category, all models exhibit low AP, while our method maintains balanced Precision and Recall, minimizing false alarms and misses.