## 3 SEM-RCNN-Based EM Detection Method
### Feature Extraction Step
#### 3.1.2 SENet  
SENet is a network structure that focuses on enhancing data channel information and enhancing desired feature information while suppressing less useful feature information based on the self-attention mechanism. SE block is the basic module of SENet and can be well integrated with many existing models. The corresponding experimental classification and detection results show that the combination with an SE block can increase the feature representation capability of the model and, thus, improve the classification or detection effects of the model. The basic structure of the SE block is shown in Figure 4. Among them, \(F_{tr}\) is the traditional convolution operation. \(X\) and \(U\) represent the input and output of this convolution operation, respectively; \(C\), \(W\), \(H\), \(C^{\prime}\), \(W^{\prime}\), and \(H^{\prime}\) represent the scales of data;  
Figure 3: The structure of residual block. function \(F_{sq}\) and \(F_{ex}\) represent the two core processes of the SE block, squeeze processing and excitation processing; \(\vec{X}\) represents the final output of the SE block. In the SE process, a squeeze operation is first performed on the convolution output. To make better use of the interconnected information between input data channels, the SE block first uses the idea of averaging to convert the information of all pixels involved in a plane into a specific value. The specific calculation procedure for averaging is shown in Equation (1). \[z_{c}=F_{sq}(u_{c})=\frac{1}{H\times W}\sum_{i=1}^{H}\sum_{j=1}^{W}u_{c}(i,j) \tag{1}\]  
In Equation (1), \(z_{c}\) represents the squeeze output of the \(c\) channel data of input data; \(u_{c}\) represents the \(c\) channel data of input data. It can be seen from the formula that input data eventually become a column vector in the squeeze process. The length of this vector is the same as the number of channels, and each data value in this vector is closely related to the corresponding channel data. After that, to further exploit the interlinked information between channels, SE designs an excitation process. The equation for this process is shown in Equation (2). \[s=F_{ex}(z,W)=\sigma(g(z,W))=\sigma(W_{2}\sigma(W_{1}z)) \tag{2}\]  
In Equation (2), \(\sigma\) is the rectified linear unit (ReLU) activation function; \(W_{1}\in\mathbb{R}^{\widehat{\times}\times C}\); \(W_{2}\in\mathbb{R}^{C\times\widehat{\times}}\); \(W_{1}\) is the dimensionality reduction layer with a dimensionality reduction ratio of \(r\). \(W_{2}\) is the proportionally identical data-dimensionality increase layer. After the excitation process, the complexity of the entire model is controlled. Moreover, vital features are enhanced, and weak features are limited based on the self-attention mechanism. Moreover, the generalization  
more suitable for predicting the category of the object. Therefore, a suitable combination of shallow and deep features can achieve the accurate detection of EMs, which is the main idea of FPN. FPN consists of three main network structures: down-up processing, up-down processing, and horizontal linkage of feature layers, as shown in Figure 5.  
Down-up processing is the feedforward process of CNN. By non-stop convolutional operations, multiple feature maps of different scales are obtained in this step. After that, the feature maps output by the deepest level network are up-sampled by the operation to keep the same size as the feature maps output by the previous level network. Then, upsampled feature maps are fused with the feature maps outputted by the previous layer network. Feature maps obtained based on FPN are richer in feature information than traditional feature maps. In addition, to better fuse the information of each map, FPN processes the fusion between feature maps by using the horizontal linkage operation. The horizontal linkage is performed by convolutional processing using a \(1\times 1\)-sized convolutional kernel.