## 5 Conclusions  
The CRTD network proposed in this article demonstrates significant performance improvements for ship detection tasks in optical satellite images under thin cloud coverage. By integrating the Thin Cloud Removal (TCR) and Small Object Detection (STD) models, this method effectively overcomes the challenges posed by low contrast and occlusion in thin cloud-covered images, enabling accurate detection of ship targets in cloud and fog interference conditions. The experimental results show that the TCR module successfully enhances the clarity and contrast of images in the preprocessing stage, while the STD module excels in object recognition. Through a dual-level supervision mechanism, the combined efforts of the TCR and STD modules lead to improved performance, especially in thin cloud coverage scenarios, thereby enhancing ship detection accuracy. Further experimental comparisons demonstrate that CRTD outperforms popular detection models such as YOLOv5 and Faster R-CNN in terms of accuracy and recall. In practical detection scenarios, CRTD effectively reduces false positives and false negatives. Ablation experiments validate the contributions of various components (such as TCR, GARM, and CCIB), confirming that each module plays a crucial role in the overall performance improvement. CRTD performs particularly well in detecting weak targets under complex weather conditions. However, this study has certain limitations. The relatively small sample size and challenges in obtaining labeled data for small targets under varying environmental conditions may limit the generalizability of the results. While CRTD demonstrates strong performance, particularly under thin cloud cover, there is potential for further improvement. Future work will focus on expanding the dataset to include a broader variety of ship types, weather conditions, and geographic regions, which will help to further validate the robustness and generalizability \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **Baseline** & **STD** & **CCIB** & **GARM** & **P (\%)** & **R (\%)** & **AP (\%)** \\ \hline
1 & ✓ & - & - & - & 90.77 & 80.30 & 82.21 \\
2 & ✓ & ✓ & - & - & 91.96 & 80.65 & 85.65 \\
3 & ✓ & - & - & ✓ & 92.37 & 81.33 & 86.82 \\
4 & ✓ & ✓ & - & ✓ & 92.74 & 81.88 & 86.59 \\
5 & ✓ & ✓ & ✓ & ✓ & 93.23 & 82.44 & 88.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of different methods. of the network. Additionally, efforts will be made to enhance performance in more complex weather scenarios, such as heavy clouds, fog, and low-resolution satellite imagery. Future research will also explore the integration of advanced image preprocessing techniques to complement the TCR module, which could improve its ability to handle a wider range of atmospheric disturbances. Moreover, refining the detection of weak and small targets in cluttered environments remains a critical challenge in satellite-based detection systems. In conclusion, this study presents a promising solution for ship detection under complex weather conditions, highlighting the potential of deep learning for remote sensing applications. While the current work provides valuable insights, addressing the aforementioned limitations will be key to enhancing both the accuracy and applicability of the CRTD network in real-world scenarios. These improvements will help extend the network's capabilities and contribute to more accurate ship detection in diverse environmental conditions. Methodology, F.L. ; Software, F.L. ; Validation, M.W. and Q.X. ; Formal analysis, F.Z. ; Resources, M.W. ; Data curation, Q.X. ; Writing--original draft, F.Z. All authors have read and agreed to the published version of the manuscript. This work was supported by the Open Research Fund of the State Key Laboratory of Target Vulnerability Assessment, Defense Engineering Institute, AMS: ASM20240726. Not applicable. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to confidentiality.