## 5 Conclusions  
In this paper, we present an improved object detection method for SAR images, based on modifications to YOLOv8. The SAR images used in this study were captured by satellites such as GaoFen-3, with a vertical viewing angle, and included four target types: ships, bridges, oil tanks, and planes. These targets are often blended into complex backgrounds, such as mountains, harbors, airports, and sea surfaces. Additionally, SAR images feature multi-scale targets with irregular shapes, making it challenging to extract features for accurate detection and recognition. As a result, the recall rate is difficult to improve using the base YOLO model, which is typically trained on optical images with clear contours and boundaries. To address these challenges, we introduce several enhancements to YOLOv8. A modified FPN-PAN network is employed to improve the detection of tiny objects, incorporating four detection headers, which differ from the basic YOLOv8 structure. To enhance precision, a deformable large kernel attention (DLKA) module is added to layer 8 of YOLOv8's backbone. This module focuses on fine details of targets using a deformable and adaptive large convolutional kernel. Additionally, a dynamic convolution module is used to replace standard convolution in layers 2 and 4, improving YOLOv8's training efficiency. Results from ablation experiments demonstrate that combining DynamicConv with other modules significantly increases both precision and recall rates compared to using only the M-FPN-PAN or DLKA module.  
In summary, the improved method, named MDD-YOLOv8, achieves a precision of 87.7% and a recall of 76.1%, representing improvements of 8.1% and 6.0%, respectively, over the basic YOLOv8. Across categories, MDD-YOLOv8 shows superior performance in detecting ships and planes compared to bridges and oil tanks. This is because bridges and oil tanks exhibit weaker features, making them more prone to confusion with backgrounds. For example, bridges are often mistaken for roads, and oil tanks can be misclassified as buildings. Despite these challenges, MDD-YOLOv8 consistently outperforms other models across all classes. Furthermore, generalization experiments conducted on four additional SAR image datasets confirm MDD-YOLOv8's effectiveness and adaptability in detecting objects from satellite-acquired SAR images. However, recall rates for bridges and oil tanks remain relatively low, indicating room for further improvement. In the future, we plan to reduce the number of network parameters to make MDD-YOLOv8 more lightweight, thereby improving training and prediction speeds. Additionally, we will test the model on more diverse SAR image datasets to further enhance its generalization capabilities.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Datasets** & **Origin** & **Yolov5s** & **Yolov8n** & **Yolov9t** & **Rt-detr-1** & **Ours** \\ \hline SRSDD-v1.0 [40] & & & & & & \\ \hline RSDD-SAR [41] & & & & & & \\ \hline FUSAR-Ship [42] & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 9: _Cont._  
**Author Contributions:** Conceptualization, J.L.; methodology, X.L. and J.L.; software, J.L.; validation, X.L; formal analysis, S.L.; investigation, J.L.; resources, J.L.; data curation, X.L.; writing--original draft preparation, J.L.; writing--review and editing, J.L.; visualization, S.L. and J.L.; supervision, H.C.; project administration, H.C.; funding acquisition, H.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.