# YOLO-SWD--An Improved Ship Recognition Algorithm for Feature Occlusion Scenarios
## 6 Conclusions and Future Work  
This paper addresses the challenges of small-target crowding and occlusion in complex ship scenarios by proposing an improved ship detection and recognition algorithm based on YOLOv11. The main contributions include the following: (1) DLKA introduces dynamic deformable convolution and large-receptive-field attention mechanisms, enhancing local detail and global contextual modeling; (2) StripPooling improves the perception of targets in complex scenes. (3) WTConv strengthens feature extraction diversity and robustness.  
Experimental results indicate that the improved YOLOv11 model--YOLO-SWD--further enhances recognition accuracy for ships in occluded scenarios within remote sensing imagery and strengthens the anti-interference capability of the YOLO-based ship detection algorithm in complex environments.  
This study investigates ship detection and recognition in remote sensing images but still faces unresolved issues requiring further research:  
1. Persistent ship tracking: Research on continuous ship tracking in image sequences requires further exploration.
2. Rotatable bounding boxes: Current detection algorithms do not account for bounding box rotation.
3. Model lightweighting: The current architecture remains relatively complex, posing deployment challenges.  
Future work should focus on integrating rotatable detection boxes to improve ship localization accuracy, especially in scenarios with varying ship orientations and dense distributions. Additionally, the current algorithm should be extended to include ship tracking functionality, enabling the model to track ships in complex occlusion environments. This can be achieved by incorporating multi-frame information, feature association, and temporal consistency to ensure accurate tracking in dynamic and obstructed settings. Furthermore, simplifying the model to reduce computational complexity while maintaining performance will be essential for real-time deployment on resource-constrained devices.  
Conceptualization, R.Z. and M.G.; methodology, R.Z. and M.G.; software, M.G.; validation, R.Z.; formal analysis, R.Z.; investigation, M.G.; resources, M.G.; data curation, M.G.; writing--original draft preparation, M.G. and R.Z.; writing--review and editing, M.G. and H.P.; visualization, R.Z.; supervision, R.Z. and H.P.; project administration, R.Z. and H.P.; funding acquisition, R.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Datasets Source** & **Methods** & **Achievement** \\ \hline Ours & \begin{tabular}{c} Gaofen-2 \\ Gaofen-3 \\ RadarSat-2 \\ Sentinel-1 \\ Sentinel-2 \\ Jilin-1 \\ SPOT \\ Google Earth \\ \end{tabular} & \
\begin{tabular}{c} (1) DLKA introduces dynamic \\ deformable convolution and \\ large-receptive-field attention \\ mechanisms, enhancing local detail \\ and global contextual modeling; \\ (2) StripPooling improves perception \\ of targets in complex scenes; \\ (3) WTConv strengthens feature \\ extraction diversity and robustness. \\ \end{tabular} &
\begin{tabular}{c} Enhance model robustness in \\ complex scenarios and improve \\ accuracy for occluded ship targets in \\ multi-source images caused by \\ weather and interference obstruction. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._Data Availability Statement: The dataset is currently non-public due to ongoing research. We plan to release it after further improvements addressing existing limitations in this work.  
The authors declare no conflicts of interest.