## 3 Proposed Methods
### Improve the C2f Module  
Introduce EMA, an efficient multi-scale attention module that operates without the requirement of dimension reduction. Its core mechanism is to smooth the attention distribution through the exponential moving average method, thereby improving the model's robustness and generalization ability. This module recalibrates the channel weights in each parallel branch by encoding global information and captures pixel-level relationships [20] through cross-dimensional interactions. EMA is designed to reduce computational overhead while preserving key information from each channel, enhancing the model's capability to process features effectively. By reorganizing the channel and batch dimensions [21] and leveraging cross-dimensional interactions, the model is able to effectively capture pixel-level relationships, which enables it to excel in focusing on important information within the image. Particularly noteworthy is the structure of the EMA attention mechanism module, as depicted in Figure 4.  
Figure 3: Structural Comparison Diagram of CA and RFCA. Specifically, EMA initially separates the feature map across the channel dimension into G sub-features, with the aim of facilitating the model in learning and capturing different semantic information, as shown in Equation (1). \[X=[X_{1},X_{2},X_{3},\cdots,X_{(G-1)}],X_{i}\in R^{(\frac{C}{6}\times H\times W)} \tag{1}\]  
In this section, \(X\) represents a three-dimensional tensor containing feature information; \(C\) denotes the quantity of input channels; \(G\) represents the sub-features divided; and \(R\) represents the set of real numbers. Secondly, EMA utilizes three parallel paths to extract clustered feature maps, with two of the paths being \(1\times 1\) branches. When encoding the channels, pooling kernels of sizes \((1,W)\) and \((H,1)\) are applied in the vertical and horizontal directions, individually, and the calculations are shown in Equations (2) and (3). \[Z_{C}^{W}(W)=\frac{1}{H}\sum_{0\leq i\leq H}X_{C}(j,W) \tag{2}\]  
\[Z_{C}^{H}(H)=\frac{1}{W}\sum_{0\leq i\leq W}X_{C}(H,i) \tag{3}\]  
In this section, \(Z_{C}^{W}(W)\) signifies the output of the \(C\)-th channel that has a width of \(W\), processed only in the vertical direction; \(X_{C}\) represents the output of the \(C\)-th channel; \(Z_{C}^{H}(H)\) signifies the output of the \(C\)-th channel that has a height of \(H\), processed only in the horizontal direction; and \(i\) and \(j\) represent different spatial directions. Figure 4: EMA Network Structure. The feature projections in the vertical and horizontal directions are fused through Equations (4)-(6), allowing different cross-channel information interactions between the two parallel paths. The fused feature representation is then split into two independent tensors, and finally, two nonlinear Sigmoid functions are applied. \[f=P([Z_{\text{C}}^{H}(H),Z_{\text{C}}^{W}(W)]) \tag{4}\]  
\[g^{h}=\sigma(f^{h}) \tag{5}\]  
\[g^{w}=\sigma(f^{w}) \tag{6}\]  
In this section, \(f\) represents the fused feature representation, where \(f\in R^{C//G\times(H+W)}\); \(P\) represents the transformation used for adjusting the number of channels; \(f^{h}\) and \(f^{w}\) represent the feature representations of \(f\) in the two spatial directions; \(\sigma\) stands for the Sigmoid activation function; and \(g\) signifies the attention weight. Upon acquiring the attention weights \(g\) in different spatial directions, they are processed together with the output of the third branch (\(3\times 3\)) using a two-dimensional global average downsampling operation to handle the feature information in the vertical and horizontal directions. The operation of two-dimensional global pooling is shown in Equation (7). Finally, cross-spatial information fusion is performed to obtain the final attention weight output of EMA. \[z_{c}=\frac{1}{\text{H}\times W}\sum_{j=1}^{W}\sum_{i=1}^{H}x_{c}(i,j) \tag{7}\]  
In this section, \(Z_{\text{C}}\) signifies the output corresponding to the c-th channel.