## 5 Conclusions  
A new method for underwater object classification is studied in this paper. In this method, Markov random field theory is used to segment the highlight and shadow regions of the underwater sonar image respectively, which preserves the complete information of the object compared with some methods that segment highlight the region and shadow simultaneously. Secondly, the depthwise separable convolution is used to automatically extract the features of the two parts, which reduces the computation and complexity of the network, avoids the problem of incomplete manual feature extraction, reduces the classification time, and improves efficiency. In addition, the feature fusion is realized by parallel network structure, which makes good use of the features of shadow and highlight regions. According to the characteristics of sonar imaging and the real shape of the objects, the simulation data set is established, which is more effective for underwater target classification tasks when using transfer learning.  
In conclusion, this method makes use of the advantages of depthwise separable convolution in feature extraction, avoids the defects of manual feature extraction, could adapt to different image sizes of the dataset, and makes full use of the effective information in the images. The experimental results are as follows: (1) Compared with VGG16 and Resnet50 models, the classification model with feature extraction network designed in this paper has better overall classification performance. The classification accuracy reaches 90.1%, the average calculation cost is reduced by dozens of times, and the model is the lightest with only 1.9 M. (2) The classification results of the proposed classification method based on depthwise separable convolution feature fusion is higher than that of the single-channel network. The accuracy is improved by 7.9% and 12.7% on average in the simulation and real sonar images, which also proves the effectiveness of the parallel network architecture. (3) In the case of partial data missing, the method proposed in this paper is still effective, and the classification accuracy can reach at least 80%, indicating that the network has certain robustness.  
However, there are also some limitations and deficiencies in the research process. For example, there is a certain gap between the classification accuracy of the simulated and the real sonar image. It is the essential difference between the two images that causes this difference, which also provides an important idea for future research. Therefore, our subsequent research will focus on the following aspects. Firstly, the characteristics of an object in sonar images will be studied to find a more reliable image simulation method. Secondly, a more effective feature extraction network will be designed according to the sonar images. Finally, a classification method suitable for the complex underwater environment will be proposed to achieve object classification.  
[PERSON] gave academic guidance to this research work and put forward feasible suggestions for the research content. At the same time, the manuscript has been modified by [PERSON], in the content and structure of the article has given a reasonable method of modification. [PERSON] designed the core method proposed in this paper, wrote the program, carried out relevant experimental verification, and drafted the manuscript. All authors have read and agreed to the published version of the manuscript. This research was funded by Institute of Acoustics, Chinese Academy of Sciences, under a project entitled, \"Intelligent Classification of Underwater Objects in Sonar Images\". The number of the funding is E1511301.