# DSA-SOLO: Double Split Attention SOLO for Side-Scan Sonar Target Segmentation
## 1 Introduction
The clustering segmentation method needs to adjust the parameters for different scenes to achieve good results, the level-set segmentation method is sensitive to noise, and the MRF model is computationally expensive. Nowadays, deep learning algorithms have been widely employed in the field of image segmentation, detection and tracking, and a large number of excellent convolutional neural network (CNN) methods such as VGG-Net [9], GoogLeNet [10] and ResNet [11] have been proposed. Meanwhile, various image segmentation networks have also been proposed, such as FCN [12], U-Net [13], RefineNet [14], and DeepLabV3 [15]. U-Net is an excellent encoder-decoder framework, which can use fewer training images to achieve better segmentation accuracy. DeepLabV3 is built on ResNet and employs dilated convolution of different rates to expand the receptive field. In addition, DeepLabV3 adds atrous spatial  
Figure 1: Several images in the dataset SCTD used in this paper. The principle of imaging sonar systems leads to high noise interference, weak boundary information, and difficult target feature extraction of sonar images. pyramid pooling (ASPP) in the last block of ResNet, which improves the ability to extract multi-scale information. However, most of these networks are all based on RGB images and are not very suitable for other imaging systems. Considering the differences between sonar images and RGB images, many CNN-based methods were proposed for sonar image segmentation. [PERSON] et al. [16] proposed an encoder-decoder network for sonar target detection and segmentation named MB-CEDN, in which an encoder model is used for extracting features of a sonar object, and then a dual decoder model is used for pixel-level segmentation. Each decoder learns the target features from different perspectives and aggregates the obtained features to a deep resolution network to refine the segmentation. [PERSON] et al. [17] combined Recurrent Convolutional Neural Networks with Residual Convolutional Neural Networks and proposed a new network called the R\({}^{2}\)CNN module. The proposed R\({}^{2}\)CNN is used to acquire sonar image features, and the self-guiding module is used to improve the segmentation robustness and accuracy. [PERSON] et al. [18] proposed a new convolutional neural model. Firstly, a depth-separable based residual module is introduced for extracting multi-scale features, and then an adaptive supervision model is used for classifying the pixels. In addition, adaptive transfer learning is utilized in the training process, which enhances generalization ability and robustness. The above CNN-based sonar image segmentation methods are all semantic segmentation methods, which classify the images pixel by pixel to achieve the purpose of segmentation. In this paper, we conducted the sonar target segmentation task as instance segmentation. Instance segmentation pays more attention to segmentation targets, which includes two steps: semantic segmentation and object detection. This means that instance segmentation can obtain the object category as well as the object mask. Considering the weak boundary information, high noise interference, and poor texture feature of sonar images (shown in Figure 1), segmentation networks need to focus on both spatial and channel features. To this end, we designed a new attention mechanism named the Double Split Attention (DSA) module to enhance the performance of the network, drawing inspiration from ShuffleNetV2 [19] and CBAM [20]. DSA fuses the spatial and channel attention mechanisms, and focuses the network on the boundary feature and spatial location of the sonar target. The main contributions of this paper are summarized as follows:  
1. We proposed a novel model named DSA-SOLO for side-scan sonar image instance segmentation, and experimentally demonstrated its effectiveness in the object segmentation of side-scan sonar images. 2. We proposed a DSA module which fuses spatial and channel attention together to extract the target feature.