# Side-Scan Sonar Image Augmentation Method Based on CC-WGAN
## 1 Introduction  
With the continuous exploitation of marine resources, an increasing number of detection technologies have been applied to underwater target detection [1], maritime search and rescue [2], navigation safety, marine engineering [3, 4], seabed mapping, and other fields. Currently, side-scan sonar systems, owing to their affordability and superior scanning precision, have obvious advantages for seabed target identification and are the main technology for underwater target detection [5]. The advancement of deep learning technology in computer vision has significantly improved target detection performance and has been widely used in the underwater intelligent detection field. However, deep convolutional neural network (DCNN) technology requires a substantial amount of high-quality sample data that are representative to train target detection models [6]. Due to the elevated costs associated with measurements and the limited occurrence of maritime events, there is a scarcity of side-scan sonar image samples, which also lack representativeness. This dearth constrains the progress of DCNN in underwater target detection. Consequently, there is an immediate requirement for research on augmenting samples for few-sample underwater targets in SSS images. To address the issue of limited SSS images, some scholars have utilized techniques for image transformation augmentation in order to expand the number of samples [7, 8]. Nevertheless, these artificially generated samples are deficient in terms of representativeness due to their failure to account for variations in imaging conditions and environmental factors. This deficiency has a negative impact on the enhancement of accuracy in recognizing underwater targets. Inspired by sample augmentation techniques in optical imaging, manyresearchers have used transfer learning methods for SSS underwater target sample augmentation. However, these methods do not consider the imaging mechanism of side-scan sonar during the transfer conversion model training [9; 10], resulting in samples being generated with single styles that have poor representativeness, thus providing limited improvement in target detection models' performance. In recent years, with the rapid advancement of GAN networks, they have been extensively employed to produce numerous high-quality image samples. The core of GAN is to generate high-quality images so that the discriminator cannot distinguish between real and fake by adversarially training the generator and discriminator, having stronger and more comprehensive feature extraction and learning capabilities than traditional machine learning. [PERSON] et al. [11] presented a thorough sample augmentation approach considering the target, texture, resolution, noise, and background to solve the problem of insufficient training samples for shipw detection. However, the overall implementation process is complex. [PERSON] et al. [12] introduced a technique for enhancing underwater target images in side-scan sonar data through cross-domain mapping of corresponding target images. However, due to the need for actual sea trials, this method is costly and complex. [PERSON] et al. [13] introduced a self-attention network called multi-feature fusion (MPSANet) for the creation of novel side-scan sonar image varieties. [PERSON] et al. [14] introduced a novel global context external attention network (GCEANet) for producing side-scan sonar images that align with absent categories in image classification. [PERSON] et al. [15] proposed an SSS image augmentation approach that is suitable for multi-task scenarios. However, cross-domain transformation sample augmentation methods produce samples with weak representativeness due to the similarity but not exact match of targets in non-domain images to actual underwater targets to be detected. [PERSON] et al. [16] proposed a method that utilizes the imaging mechanism and image features of side-scan sonar to simulate samples of SSS images, incorporating a ray model and sonar equation.