# YOLO-SWD--An Improved Ship Recognition Algorithm for Feature Occlusion Scenarios
## 5 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **DLKA** & **CKSP** & **WTConv** & **Category** &
\begin{tabular}{c} **Precision** \\ **(\%)** \\ \end{tabular} & **Recall (\%)** & **AP (\%)** & **mAP (\%)** \\ \hline \multirow{4}{*}{Original} & \multirow{4}{*}{} & & DDG & 80.6 & 88.5 & 90.5 & \multirow{4}{*}{81.2} \\  & & & CVN & 96.5 & 97.6 & 99.1 & \\  & & & CG & 74.0 & 90.4 & 91.0 & \\  & & & LST & 84.8 & 79.2 & 84.6 & \\  & & & CV & 79.6 & 51.8 & 57.8 & \\  & & & SP & 100 & 58.8 & 66.5 & \\ \hline \multirow{4}{*}{Scheme 1} & \multirow{4}{*}{\(\surd\)} & & DDG & 76.9 & 88.1 & 89.8 & \multirow{4}{*}{82.5} \\  & & & CVN & 95.6 & 98.6 & 99.4 & \\  & & & CG & 70.4 & 94.5 & 90.5 & \\  & & & LST & 73.7 & 83.0 & 87.1 & \\  & & & CV & 59.4 & 56.0 & 55.6 & \\  & & & SP & 100 & 54.7 & 72.6 & \\ \hline \multirow{4}{*}{Scheme 2} & \multirow{4}{*}{\(\surd\)} & & DDG & 79.1 & 89.3 & 90.6 & \multirow{4}{*}{83.1} \\  & & & CVN & 94.1 & 98.1 & 99.3 & \\ \cline{1-1}  & & & CG & 75.7 & 94.3 & 91.7 & \\ \cline{1-1}  & & & LST & 85.3 & 81.1 & 83.2 & \\ \cline{1-1}  & & & & CV & 68.6 & 49.0 & 54.5 & \\ \cline{1-1}  & & & & SP & 100 & 71.2 & 79.6 & \\ \hline \multirow{4}{*}{Scheme 3} & \multirow{4}{*}{\(\surd\)} & & & DDG & 83.7 & 85.4 & 90.7 & \multirow{4}{*}{83.9} \\  & & & & CVN & 95.4 & 98.6 & 99.3 & \\ \cline{1-1}  & & & CG & 74.7 & 90.9 & 91.9 & \\ \cline{1-1}  & & & & LST & 81.9 & 79.2 & 85.3 & \\ \cline{1-1}  & & & & CV & 68.5 & 53.5 & 58.1 & \\ \cline{1-1}  & & & & SP & 100 & 71.7 & 78.1 & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative comparison of ablation experimental results. both the local and global levels. DLKA focuses on critical regions, CKSP enhances spatial and structural comprehension through StripPooling, and WTConv allows for more robust feature extraction across scales. As a result, the overall detection accuracy and stability improve, with consistent improvements in categories like DDG, CG, LST, and CV. Despite a slight drop in SP's AP (from 79.6% to 78.1%), the overall performance demonstrates that the three modules complement each other, providing a comprehensive solution for enhanced feature capture, spatial understanding, and convolutional expression. In summary, each module contributes uniquely to the model's performance: DLKA improves fine-grained feature capture by focusing on critical regions, CKSP enhances spatial structure understanding through StripPooling, and WTConv boosts convolutional feature extraction across scales. Together, they provide complementary benefits that result in superior detection accuracy and model performance. In industries such as maritime surveillance, autonomous navigation, and satellite imagery analysis, the proposed algorithm can be leveraged to detect and recognize ships in diverse environments, including those with varying target sizes, complex structures, and occlusions. The ability to effectively handle small-sample categories and preserve spatial details ensures that the model performs well even in challenging scenarios, thus enhancing the overall reliability and performance of ship detection systems. In addition, as shown in Table 3, compared to existing methods limited to single data sources, our approach demonstrates cross-domain versatility by integrating multi-sensor data, At the same time, our framework comprehensively improves the ship interference and occlusion issues in SAR and optical images through physics-based detailed feature learning and cross-sensor adaptability, providing new insights for ship detection and recognition in occluded scenarios under all-weather, multi-source imagery.