# Research on Improved YOLO11 for Detecting Small Targets in Sonar Images Based on Data Enhancement
## 3 Methodology
### Improved Network
#### 3.2.3 C3K2_Sc  
Traditional convolutions often produce redundant features when processing sonar images with highly similar background features. This increases computational load and may cause overfitting, degrading model performance. To address this, this paper integrates ScConv [28] into the C3K2 module to form C3K2_Sc. ScConv (Spatial and Channel Reconstruction Convolution) aims to enhance network performance by optimizing feature extraction and reducing computational resource consumption; it consists of two main components: 1. Spatial Reconstruction Unit (SRU): Reduces spatial redundancy through separation and reconstruction. 2. Channel Reconstruction Unit (CRU): Reduces channel redundancy through a split-transform fuse strategy. However, the separation strategy of conventional CRU relies on a fixed channel splitting ratio, which lacks dynamic adaptation to input features and incurs risks of information loss. To address this limitation, we propose the CRU-Dynamic unit, where the compression ratio \(\alpha\) dynamically adapts to input features, thereby enhancing the completeness of channel information. Figure 9 shows the structure of ScConv:  
The structure and process of the SRU unit are shown in Figure 10. First, it receives the input feature \(X\), and then normalizes it via Group Normalization to reduce the scale differences between feature maps. Next, it calculates weights using the channels \(\gamma_{1}\)\(\gamma_{2}\)...... \(\gamma_{\text{c}}\) of the input features and a Sigmoid function. These weights \(w_{1}\)\(w_{2}\)...... \(w_{\text{c}}\) are used to weight the features. The weighted features are split into two parts \(X_{1}^{W}\) and \(X_{2}^{W}\). After transformation, the two parts are reconstructed through addition and concatenation to obtain the spatially refined feature \(X^{W}\). The architecture of the Channel Reconstruction Unit (CRU) is detailed below. Starting with the spatially refined feature \(X^{W}\), the CRU first splits \(X^{W}\) into two parts. These parts undergo different \(1\times 1\) convolution processes via paths with ratios \(\alpha\) and \((1-\alpha)\). We have improved the definition of \(\alpha\) by first introducing a feature-aware layer that extracts channel-wise statistics \(S\) from the input feature map \(X\) through Global Average Pooling (GAP). Figure 9: SC_Conv structure. A lightweight Multi-Layer Perceptron (MLP) is then designed to generate the dynamic compression ratio parameter \(\alpha\), as formally expressed in Equation (12). \[\alpha=\sigma(W_{2}\cdot\delta(W_{1}\cdot s)) \tag{12}\]  
where \(W_{1}\) and \(W_{2}\) are learnable weight matrices, \(\delta\) denotes the ReLU activation function, and \(\sigma\) represents the Sigmoid function to constrain the output range to (0, 1). Global Convolution (GWC) and Point-wise Convolution (PWC) further transform these features. Finally, the transformed features \(Y_{1}\) and \(Y_{2}\) are pooled, then weighted and fused via Softmax to produce the final channel-refined feature \(Y\). The CRU-Dynamic Unit Structure is shown in Figure 11.  
Figure 11: CRU-Dynamic Unit Structure. Figure 10: SRU Unit Structure. The key improvements of the C3K2_Sc module lie in the feature extraction stage, where the standard convolution (Conv) in the original C3K2 is replaced by the enhanced ScConv module. This not only uses a spatial reconstruction unit (SRU) to dynamically suppress activation responses in irrelevant spatial areas but also integrates a dynamic channel reconstruction unit (CRU-Dynamic) to prune the channel dimension softly, thus precisely preserving target features and reducing computational resource consumption. During the subsequent feature fusion stage, the original identity mapping design of the bottleneck structure is retained; this ensures smooth gradient propagation during deep feature fusion and effectively avoids training divergence caused by architectural mutations.