## 6 Conclusions  
In this paper, we focus on the underwater object classification in SAS images, and the structure of ResNet is optimized and redesigned to enhance the spatial sampling ability and improve the classification performance. The main contributions of the method in this study are as follows: (1) The DSDCN module can focus more on the position related to the object when extracting features, better adapt to different shapes of objects, and extract finer features than traditional convolution. (2) The depthwise separable convolution is used in the DSDCN module instead of standard convolution for the calculation of offsets, which has the advantage of being lightweight and improves the ability of feature extraction of the model. (3) The residual blocks in ResNet do not introduce additional parameters, so the complexity of the model is not affected, and the jump connection can realize information exchange between different layers. (4) By migrating the model parameters, the problem of insufficient data is effectively solved, and the classification accuracy is further improved. Several experiments were conducted under different models, and the results were as follows: (1) The classification accuracy for sonar images with the method in this study was 95.5%, which was 6.83% better, on average, compared with the baseline model ResNet_0, VGGNet, UNet, and Light-ResNet. At the same time, it had a slight advantage in terms of computational consumption. (2) In the ablation experiment of DSDCN module, the classification accuracy of the model ResNet_0 without the DSDCN module was only 83.1%, and the classification accuracy of the other four models with DSDCN added at other positions was on average 5.1% lower than the model in this paper, and the classification performance of this model was better. (3) When using deformable images for underwater object classification, the classification results of the DSDCN-ResNet in this paper had improved in accuracy, recall, and F1 scores, with an average increase of 5.3%, 5.6%, and 5.8% respectively. (4) The classification accuracy of the model trained by the transfer learning method was higher than that without the method, about 7.4%. In summary, the classification method proposed in this paper has high accuracy, compared with other models, and it has certain advantages in terms of parameters and computation. However, the study also had some limitations due to the high cost of acquiring sonar images, resulting in a small number of real images used in the experiments, and the deformable convolution module made the training time of the model longer. To \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Accuracy/\%** & **Recall/\%** & **F1 Score/\%** \\ \hline ResNet\_0 & 83.1 & 85.1 & 83.4 \\ Light-ResNet & 82.6 & 81.4 & 82.1 \\ UNet & 83.0 & 83.4 & 82.1 \\ VGGNet & 87.2 & 88.4 & 85.4 \\ This paper & 89.3 & 90.2 & 89.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of different models on deformable images. \begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Accuracy/\%** \\ \hline ResNet\_No & 88.1 \\ This paper & 95.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accuracy of model with transfer learning. solve the above problems, our future research will focus on the following: First, new data simulation methods will be studied to minimize the difference between the simulated and real images to obtain increasingly realistic simulated images. Second, the optimal feature set of the sonar images will be studied, and efficient feature extraction methods will be used to extract features; the classification accuracy of the model will be further improved for different scales and different types of objects. Finally, new model optimization methods will be investigated to reduce model training time and improve the model classification performance. [PERSON] gave academic guidance to this research work and put forward feasible suggestions for the research content. The manuscript has also been modified by [PERSON] in its content and structure.