# A Small-Sample Target Detection Method of Side-Scan Sonar Based on CycleGAN and Improved YOLOv8
## 1 Introduction  
With the continuous development of acoustic sensing technology and sonar equipment, underwater target detection using sonar images has become an increasingly important research field. Military experts, scholars, and researchers worldwide have shown great interest in this field, and underwater target detection has substantial application prospects [1]. Traditional sonar image target detection algorithms mainly include those based on statistical mathematics, mathematical morphology, and pixel-based methods [2]. However, these methods have poor detection performance and long detection times. Therefore, better methods are needed to improve detection accuracy and reduce detection time for sonar images. In recent years, as deep learning models for target detection have rapidly developed, more researchers have used deep learning methods for underwater target detection [3]. Currently, deep learning-based target detection methods have achieved excellent performance and widespread application in the field of sonar images. These methods are mainly divided into one-stage and two-stage methods, also referred to as candidateregion-based and regression-based target detection methods, respectively. One-stage methods, represented by the You Only Look Once (YOLO) series, directly obtain the object class probabilities and position coordinates [4] and show remarkably faster performance compared with two-stage methods. The detection result supplies a confidence parameter reflecting whether the target is included and a coordinate parameter describing the position of the bounding box. However, one-stage methods provide object class and position information directly through the backbone network without using the region proposal networks, so their accuracy is slightly lower than that of two-stage methods. Therefore, one-stage detection is more suited for tasks that require high real-time performance and detection efficiency and is effective for underwater target detection. Many studies have shown that YOLO series algorithms perform well in underwater target detection. [PERSON] et al. [5] proposed an underwater target detection system based on the YOLOv3 network and showed that YOLOv3 is an effective method for improving the detection accuracy of side-scan sonar targets. [PERSON] et al. [6] introduced an improved YOLOv4-based sonar target detection and classification algorithm, which greatly reduces network parameter redundancy and improves model efficiency. Additionally, [PERSON] et al. [7] proposed a real-time automatic target detection method that combines transformers with YOLOv5s and further improved the detection accuracy of side-scan sonar targets. YOLOv7 and YOLOv8 models have also been improved and achieved higher detection accuracy in side-scan sonar images [8,9]. However, due to the limited sonar datasets and insufficient image samples, the current accuracy of target detection methods of sonar images still needs to be improved. To solve the small sample problem, data augmentation methods are widely used. Existing data augmentation methods include spatial and frequency domain enhancement methods. Spatial domain enhancement methods include histogram equalization, contrast stretching, gray-level transformation, smoothing filtering, and sharpening filtering. Frequency domain enhancement methods contain Fourier transform, wavelet transform, high-frequency enhancement filtering, and low-frequency enhancement filtering. Modern data augmentation methods use deep learning algorithms, such as generative adversarial network (GAN), for generating pseudo images to augment target image samples. The application of GANs has also promoted research in sonar target image generation. [PERSON] et al. [10] proposed a method for generating pseudo-sonar images using GANs.