## 3 Method
### SC Attention Mechanism
#### 3.2.1 Spatial Attention Unit  
During the convolution process, the model may experience feature degradation, resulting in the progressive loss of small object details. This paper improves feature representation by extracting detailed information and fusing it with salient information. To avoid misinterpreting background information as either useful or useless, a scaling factor is used to control the threshold range, effectively separating the feature map into three parts, i.e., detailed information, background information, and salient information, as shown in Figure 6.  
To capture the informative content from various feature maps while minimizing the influence of batch size during training, Group Normalization (GN) [31] is used to normalize the feature maps. The scaling factor of the GN layer is then employed to measure the relative importance of information between different feature maps. In this paper, Equation (5) is used to standardize the feature map \(X\in R^{N\times C\times H\times W}\) (N stands for the batch size, C stands the channel dimension, and H and W correspond to the spatial width and height axes):  
\[X_{\text{out}}=GN(X)=\gamma\frac{X-\mu}{\sqrt{\sigma^{2}+\epsilon}}+\beta \tag{5}\]  
where \(\sigma\) and \(\mu\) are the standard deviation and mean of X, \(\gamma\) and \(\beta\) are the learnable affine transformations, and \(\epsilon\) is a small positive constant introduced to ensure numerical stability during division.  
The learnable parameter \(\gamma\in R^{C}\) of the GN layer is utilized to quantify the spatial pixel variability across each channel and batch. Greater variations in spatial pixels are reflected  
Figure 5: SC module.  
Figure 6: Spatial Attention Unit.  
in richer spatial information, leading to an increase in \(\gamma\). The normalized weight \(W_{\gamma}\in R^{C}\) is derived from Equation (6):  
\[W_{\gamma}=\{w_{i}\}=\frac{\gamma_{i}}{\sum_{j=1}^{C}\gamma_{j}},i,j=1,2,\cdots,C \tag{6}\]  
The reweighted feature maps' weights are transformed into the (0, 1) range using the sigmoid activation, generating the weight information \(W_{X}\). Subsequently, gating is applied using a threshold, where the thresholds are defined in Equations (7) and (8):  
\[\tau_{up}=\mu+\sigma*\beta \tag{7}\]  
\[\tau_{low}=\mu-\sigma*\beta \tag{8}\]  
where \(\tau_{up}\) and \(\tau_{low}\) represent the high and low thresholds, \(\mu\) denotes the mean of \(W_{X}\), \(\sigma\) represents the variance in \(W_{X}\), a trainable parameter that adjusts the threshold range. The weight exceeding the \(\tau_{up}\) threshold is assigned a value of 1, resulting in the weight \(W_{1}\) for salient information. The weight exceeding the \(\tau_{up}\) threshold or below the \(\tau_{low}\) threshold is set to 0 to obtain the weight \(W_{2}\) for background information. The weight exceeding the \(\tau_{low}\) threshold is assigned a value of 0, resulting in the weight \(W_{3}\) for detailed information. The entire process of obtaining W can be represented by Equation (9):  
\[W=\text{Gate}(\text{Sigmoid}(W_{\gamma}*GN(X))) \tag{9}\]  
The input feature X is multiplied with \(W_{1}\), \(W_{2}\), and \(W_{3}\) to obtain three weighted features: salient information features \(X_{1}^{up}\), background information features \(X_{2}^{up}\), and detailed information features \(X_{3}^{up}\). This successfully partitions the input features into three components: \(X_{1}^{up}\) contains rich and expressive spatial content, referred to as salient information, \(X_{2}^{up}\) contains irrelevant information, referred to as background information, and \(X_{3}^{up}\) contains minimal information, considered as detailed information. Finally, the weighted features \(X_{1}^{up}\), \(X_{2}^{up}\), and \(X_{3}^{up}\) are summed to generate the spatially refined feature map \(X_{s}\).