# Side-Scan Sonar Image Augmentation Method Based on CC-WGAN
## 5 Conclusions  
Addressing the issues of the scarcity, limited number of samples, and high acquisition cost of SSS underwater target images, which lead to suboptimal outcomes in the detection of underwater targets using deep learning techniques, a CC-WGAN-based side-scan sonar underwater target augmentation method is proposed. This approach amplifies the diversity and quality of the generated images. By integrating the CBAM into the generator and discriminator, the correlation between the dimensions of channels and spatial dimensions is enhanced, enhancing target learning, reducing information diffusion, and improving the model's performance and accuracy, thereby increasing the quality of the generated images. For side-scan sonar images with too few samples, image augmentation aids in sample augmentation and improves target detection performance. Additionally, this method considers the relationship between targets and shadows, which is one of the crucial pieces of information contained in SSS images. Finally, we performed an assessment of the enhanced images utilizing the FID, MMD, PSNR, and SSIM assessment criteria and conducted performance evaluation experiments for target detection using deep learning models. Ablation experiments were also set up to validate the function of each module in influencing the model's performance. It is worth mentioning that when using the CC-WGAN for image generation, the algorithm needs to run for about five days, which is slow, and the running speed of the algorithm should be improved in the future. This study explores the generation of numerous augmented images from a limited dataset, demonstrating that the use of augmented images can enhance the precision of detecting and recognizing underwater targets. This method partially mitigates the problem of limited underwater sample numbers.  
Conceptualization, J.Z. and H.L.; methodology, J.Z.; software, H.L.; validation, Y.P.; formal analysis, J.H.; data curation, J.Z.; writing--original draft preparation, J.Z.; writing--review and editing, H.L. and P.Q.; visualization, Y.P.; supervision, P.Q.; funding acquisition, H.L. and J.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science Fund for Excellent Young Scholars, grant number 42122025; the National Natural Science Foundation of China, grant number 42374050; and the Natural Science Foundation for Scholars of Hubei Province of China, grant number 2022 CFB865.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Class** & **Precision** & **Recall** & **mAP0.5** & **mAP0.50.95** \\ \hline \multirow{3}{*}{basic GAN} & ship & 84.2\% & 92.7\% & 0.948 & 0.624 \\  & aircraft & 84.9\% & 81.2\% & 0.864 & 0.515 \\  & all & 84.5\% & 87\% & 0.906 & 0.57 \\ \cline{2-6}  & ship & 94.6\% & 87.4\% & 0.954 & 0.636 \\  & aircraft & 96.7\% & 66.7\% & 0.878 & 0.598 \\  & all & 95.6\% & 77\% & 0.916 & 0.617 \\ \cline{2-6}  & ship & 96.8\% & 96.1\% & 0.961 & 0.879 \\ \cline{2-6}  & aircraft & 98\% & 94.1\% & 0.948 & 0.733 \\  & all & **97.4\%** & **95.1\%** & **0.955** & **0.806** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance evaluation experiments for target detection based on images from three generation algorithms.  
**Acknowledgments:** We would like to thank [PERSON] for his valuable suggestions and discussions that contributed to the development of this work. We appreciate the constructive feedback provided by the anonymous reviewers, which has significantly enhanced the quality of the manuscript.  
**Conflicts of Interest:** The authors declare no conflicts of interest.