## 1 Introduction  
Underwater automatic target recognition (ATR) technology is widely used in military and civil fields, and object classification in sonar images is an important research field [1, 2, 3]. The highlighted echo and black shadow in sonar images are the important attributes of the object, both of which have rich features and are the focus in the field of object classification. In ref. [4], by minimizing the Euclidean distance between the points on the shadow contour and the hyper ellipse, the shadow is fitted as a hyper ellipse. The hyper ellipse parameters are then used to achieve the classification of the object in side-scan sonar images. In ref. [5], the mean clustering method is used to separate the  
fuzzy morphology, and the object classification is achieved through the extracted shape features combined with Markov Chain Monte Carlo theory (MCMC). In addition, several other studies have used the idea of segmenting the sonar image into different parts for classification or detection [9; 10; 11], but there are some deficiencies in feature extraction. In recent years, a convolutional neural network has a mature application in the field of image processing [12; 13], such as image denoising, image restoration, and image separation. It also has been used in the classification of objects in sonar images. For example, a convolutional neural network is used [14] to classify moment features extracted from a shadow region which are obtained by graph cut algorithm. The method of transfer learning [15] is introduced to realize classification, and the requirement of the amount of data is reduced while training the network. Therefore, the use of depthwise convolution may be to improve the classification performance by joint using highlight and shadow features of the underwater object in sonar images.  
In this paper, Markov random field theory is used to segment highlight and shadow regions of the object in sonar images respectively. The features of the two regions are extracted through the depthwise separable convolution. Then, the extracted feature maps are fused at the last convolution layer. The fusion features are used to achieve the object classification through full connection layers. The contribution of the proposed method is stated below. The object information is preserved to a large extent by segmenting the object image separately. The feature extraction capability and lightweight advantage of depthwise separable convolution are utilized to improve the classification capability of the network. Pyramid pooling is added in front of the full connection layer to extract the multi-scale information. This method can also be used to achieve classification by a single region when the highlight is blocked or the shadow is missing.  
The remainder of this paper is organized as follows. In Section 2, we introduce the structure of the underwater object classification method proposed in this paper. The methods and material including the image classification method, improved depthwise separable convolution, single feature extraction network, and classification network are presented in Section 3. Data set description and experimental results for the classification tasks are demonstrated in Section 4. The conclusions of this work are highlighted in Section 5.