# Research on Improved YOLO11 for Detecting Small Targets in Sonar Images Based on Data Enhancement
## 2 Data
### Image Dataset Generation  
Faced with the challenge of insufficient training data from only 784 original sonar images, this study employs data augmentation techniques for dataset expansion. Traditional image data augmentation methods include geometric transformations such as flipping, rotation, scaling, and translation; color transformations such as brightness and contrast adjustment; and adding noise or applying filtering. However, these conventional approaches can only expand data through linear transformations or simple combinations, failing to generate novel features or semantic content beyond the original data distribution. They may even compromise image authenticity (e.g., salt-and-pepper noise and excessive sharpening) and cause models to learn invalid features. Compared to traditional methods constrained by linear transformations that may compromise image authenticity, Generative Adversarial Networks (GANs) [9; 10; 11] demonstrate distinct advantages. Through unsupervised adversarial optimization mechanisms, GANs adapt particularly well to the unique characteristics of sonar imagery: low-resolution textures, high-noise environments, and specialized underwater terrain features. Although existing studies have explored multi-scale generation networks [12] and simulation-integrated synthesis methods [13], three critical challenges persist: First, sonar image generation must integrate physical propagation characteristics. Traditional DCGAN struggles with realistic noise pattern reproduction, Pix2 Pix [14; 15] requires impractical paired dataset scales, and CycleGAN [16; 17; 18] lacks physical modeling capabilities for acoustic wave propagation. Second, multi-dimensional domain gaps exist between synthetic and real data, encompassing background features, sensor parameters, and material properties, which compromise model generalization during deployment. Third, current generative models fail to optimally balance semantic consistency with physical fidelity. To address the aforementioned challenges with maximum potential, we introduce the ADA-StyleGAN3 method. ADA-StyleGAN3, proposed by [PERSON]'s team, is a novel few-shot sonar image generation network that integrates the adaptive discriminator augmentation strategy into StyleGAN3 [19]. Compared to conventional StyleGAN3, this approach resolves the augmentation leakage issue from traditional data enhancement techniques to the generator, ensuring high stability during dataset generation under limited samples while effectively preventing overfitting problems. Despite the aforementioned efforts, it is undeniable that generative networks, such as GANs, inherently exhibit data distribution discrepancies between synthetic and real data during the autonomous synthesis of complex scenarios by learning data distributions. These discrepancies manifest widely in aspects such as background, lighting, perspectives, and other contextual variations. When training models using both real and synthetic datasets and testing on real data, such differences inevitably impact the detector's generalization capability, introducing generalization errors. Therefore, minimizing the gap between the model's error on synthetic data (source domain) and real data (target domain) becomes crucial. To address this, this study adopts a transfer learning approach based on model parameter knowledge transfer, aiming to reduce generalization errors and decrease the model's reliance on extensive real-world data. Specifically, the ADA-StyleGAN3 generative network is employed to synthesize large-scale approximate data for pre-training under the limited sample size of the sonar dataset. Subsequently, transfer training is conducted on the pre-trained model using the original real dataset to enhance generalization performance. Building on this framework, this study employs ADA-StyleGAN3 to synthesize sonar images. Ubuntu 18.04.6 is used as the workstation's operating system, with Python as the programming language.