# DSA-SOLO: Double Split Attention SOLO for Side-Scan Sonar Target Segmentation
## 1 Introduction  
Side-scan sonar systems generate sonar images in different gray levels by recording the intensity of back-scattered acoustic waves from the seafloor. As the main sensor of underwater vehicles, side-scan sonar has a wide coverage and high resolution, which can not only map the topography of the seafloor, but also image underwater objects such as shipwrecks and aircraft wrecks. However, due to ocean ambient noise, ship self-noise and reverberant noise, sonar images contain a lot of speckle noise (as shown in Figure 1). In addition, the center frequency of side-scan sonar is above several hundred kilohertz to ensure the resolution of sonar images, but the absorption of acoustic energy by seawater increases with its center frequency in square order, which makes high-frequency acoustic waves lose much energy in seawater. Therefore, the sonar images will have uneven brightness and low contrast. All of these problems affect the accuracy of sonar target segmentation. In the last decades, the research related to sonar image target segmentation has mainly included clustering [1; 2; 3; 4], level sets [5; 6], and Markov random field (MRF) models [7; 8]. Clustering segmentation obtains a feature vector describing the pixel features through the gradation parameter of the pixel itself and the statistical parameter in the vicinity thereof, and clusters the feature vector to obtain the segmentation result. [PERSON] et al. [1] extracted a novel method by combining quantum-inspired particle swarm with fuzzy clustering. This method calculated the variance of the particle swarm fitness to update the particle position, and then obtained the global optimal results by initializing the K-means clustering center with the updated position. [PERSON] et al. [2] used K-means clustering to obtain the pre-segmentation map for reducing complexity, and combined this with edge informationand region model to accelerate convergence. [PERSON] et al. [3] proposed a spatially coherent K-means clustering method, which utilizes features extracted from the input images to cluster the information in feature space. The essence of level-set-based segmentation is to solve the extreme value and then obtain the corresponding partial differential equation by combined using the active contour model. [PERSON] et al. [5] employed K-means clustering to obtain a pre-segmentation map, and then reinitialized the distance map of the initial contour to ensure the parameter's accuracy during the level-set evolution. Finally, a robust variational level set model is used to achieve segmentation. [PERSON] et al. [6] proposed a novel level-set method which is region based. Specifically, sonar textures are extracted by a series of filters, and the similarity of these textures is calculated to get the final segmentation results. The MRF-based segmentation model generates segmentation results by modeling the labeled and observed fields in a Bayesian framework based on criteria such as maximum posterior probability. [PERSON] et al. [7] introduced a novel method in which a sonar image was described using gray value. In the proposed method, C-means clustering is used to pre-segment sonar images into three categories, including target, shadow and background. Next, the hierarchical MRF model is employed to segment the sonar images into regions of interest (RoI) and background regions, where targets and shadows are collectively referred to as RoI. Finally, RoI is re-segmented into two classes using the gray-scale thresholding method to achieve real-time segmentation of sonar images. [PERSON] et al. [8] proposed a new model based on active contours for sonar image segmentation, in which, firstly, pixel energy is calculated by itself and its neighbors against the noise and object boundary contamination, and secondly an MRF model and Bayesian framework are used to counteract intensity in homogeneity, and finally a new energy function is employed to the level set. Although the above sonar segmentation methods can achieve better segmentation results, they have shortcomings.