## 5 Conclusions  
This paper proposes a Conditional Dual-Branch Attention Generative Adversarial Network (CDBA-GAN) for synthesizing high-fidelity sonar images under specific category conditions. The proposed framework introduces two core innovations. Conditional fusion architecture: Category-specific semantic information is encoded and fused with the generator's input, enabling precise control over the generation of sonar images with predefined categorical attributes. Dual-branch attention mechanism: A hierarchical feature fusion strategy is implemented to simultaneously model channel-wise interdependencies and spatial correlations, thereby enhancing feature discriminability while preserving contextual coherence. To validate the effectiveness of CDBA-GAN, a comprehensive evaluation was conducted on a custom sonar dataset. Quantitative comparisons of the Frechet inception distance (FID) metric across five methods--DCGAN, WGAN, SAGAN, CAM-enhanced GAN, and the proposed CDBA-GAN--demonstrate the superior fidelity of images generated by our method. Furthermore, the framework exhibits robustness against noise interference, outperforming state-of-the-art benchmarks by a significant margin. Sonar data were generated to solve the difficulty that the small amount of sonar data makes it hard to improve subsequent tasks such as detection and recognition. The experiments show that our method is better than several other methods on the FID. Subsequently, we will use the generated data for sonar target detection to verify the practical value of the generated data. While the proposed method achieves notable performance improvements, its computational complexity presents a practical limitation for real-time applications. Future work will prioritize model lightweighting through techniques such as network pruning or knowledge distillation. Additionally, we plan to extend this architecture to other conditional image generation tasks, including underwater optical image synthesis and multi-modal sensor data augmentation.  
All authors made significant contributions to this work. [PERSON] designed the research and analyzed the results. [PERSON] assisted in the manuscript preparation work and validation work. [PERSON] and [PERSON] provided advice for the preparation and revision of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Stable Supporting Fund of Acoustic Science and Technology Laboratory (JCKYS2024604 SSJ00502), National Natural Science Foundation of China (U20B2074, 62471169).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
This work was supported by the Stable Supporting Fund of Acoustic Science and Technology Laboratory (JCKYS2024604 SSJ00502), which is gratefully acknowledged.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Metric**} & **CDBA-GAN** & **Label0** & **Label1** & **Label2** & **Label3** \\ \cline{2-6}  & **Feature Size** & **No Noise** & **No Noise** & **No Noise** & **Gaussian** \\ \hline \multirow{4}{*}{FID} & 16 & 105.36 & 157.74 & 124.86 & 164.55 \\  & 32 & 62.776 & 77.98 & 91.83 & 90.74 \\ \cline{1-1}  & 64 & **45.89** & **63.65** & **51.81** & 120.28 \\ \cline{1-1}  & 128 & 78.29 & 77.64 & 70.07 & **78.83** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of adding dual-branch attention module to different scales of feature maps.