# Research on Improved YOLO11 for Detecting Small Targets in Sonar Images Based on Data Enhancement
###### Abstract
This innovative approach employs end-to-end training mechanisms to autonomously extract hierarchical abstract features from large-scale annotated datasets, effectively overcoming the intrinsic limitations of conventional methods, including subjectivity in feature design and weak environmental adaptability. At present, deep learning-based target detection has three paradigms: one-stage, two-stage, and transformer-based detection. Two-stage models first use candidate box extraction to identify Regions of Interest (RoI), then perform recognition and localization. They are accurate but slow, so they are not ideal for real-time detection. This led to the development of one-stage algorithms, such as YOLO. As YOLO evolved, its accuracy on public test sets approached that of two-stage algorithms. YOLO is making waves across various fields, including sonar image target detection. [PERSON]'s ScEMA-YOLOv8 model for underwater sonar target detection uses an EMA attention mechanism and an SPPFCSPC pooling module to extract features from blurred targets better. This model also adds detection layers and residual connections to improve the detection and positioning of small targets. Regretfully, it is not optimized for targets with scarce features, and simply adding a detection layer is not enough to handle scenes with drastic changes in target scale [6]. [PERSON] and [PERSON]'s DA-YOLOv7 model for underwater sonar image target recognition features innovative modules, such as an omnidirectional convolutional channel prior convolutional attention efficient layer aggregation network, spatial pyramid pooling channel shuffle, and ghost shuffle convolutional enhanced layer aggregation network. These reduce computational load and improve the capture of local features and critical information. However, it still lacks optimization for strong noise interference in sonar images and for detecting small targets [7]. [PERSON]'s team [8] used CycleGAN for data augmentation and integrated a global attention mechanism into the feature extraction phase of YOLOv8, achieving some engineering success. Based on the above-mentioned challenges in current sonar target detection and the strategies used by previous researchers, this study carries out the following work:  
1. The experiments collected a sonar dataset with two target categories. To address the data scarcity issue, ADA-StyleGAN3, which excels in generating high-quality sonar images, was introduced. The implementation had two stages: first, pre-training models using high-fidelity synthetic data from ADA-StyleGAN3; then, fine-tuning the network parameters with real underwater data. Experiments showed that this approach effectively improved detection accuracy. 2. The FASFFHead with four detection heads is proposed. It uses ASHF for feature fusion, forming a cross-scale adaptive weighting mechanism. Differentiable spatial pyramid pooling for multi-level feature integration better preserves the semantic info of tiny targets. 3. The Edge-Contour Attention Mechanism (EEA) proposed in this study innovatively incorporates a dual-branch gradient guidance architecture, which substantially enhances gradient response characteristics at target edges through differentiated gradient information processing. This approach achieves deep integration with multi-scale deformable convolution technology, enabling adaptive focusing on critical contour regions while effectively suppressing boundary reverberation noise interference. 4. Through structural reorganization and optimization of the SC_Conv convolution module, this study innovatively constructs a cross-module collaborative architecture that integrates multi-level feature interaction and fusion with the C3K2 module, forming a feature extraction unit with a dual-path compression mechanism. The joint-optimized architecture achieves high-fidelity feature representation via a channel-spatial co-attention mechanism, effectively suppressing inter-channel correlation redundancy and spatial dimension information redundancy while enhancing feature extraction precision. 5.