## 3 The Design of SE-ResNeXt  
To conclude the monitoring image features more accurately and adequately, then achieve higher accuracy and efficiency of the automatic recognition, the SENet and multi-branch aggregation transformation are merged and improved to form the SE-ResNeXt structure in this section.  
Figure 3: Structure diagram of SE block (adapted from [18]).  
The basic module is shown in Figure 4.  
Firstly, the feature of x is extracted and merged by the convolution group in the module, and the feature map, whose channel number is c, is obtained. A Squeeze operation on the feature map is compressed by using global average pooling to obtain a real number column of \(1\times 1\times\mathrm{c}\). Then, the interdependencies between the channels are captured to obtain scale factors through an adaptive recalibration process. Finally, the features are recalibrated through the scale operation.  
Table 1 shows the network structure configuration of ResNet-50 and SE-ResNeXt-50.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Output** & **ResNet-50 1 ** & **SE-ResNeXt-50 1 ** \\ \hline \(112\times 112\) & Conv2  & \(7\times 7\), 64, stride 2 \\ \hline \multicolumn{3}{c}{Max pool, \(3\times 3\), stride 2} \\ \cline{2-3} \(56\times 56\) & \(\left[\begin{array}{c}\mathrm{conv},1\times 1,64\\ \mathrm{conv},3\times 3,64\\ \mathrm{conv},1\times 1,256\end{array}\right]\times 3\) & \(\left[\begin{array}{c}\mathrm{conv},1\times 1,128\\ \mathrm{conv},3\times 3,128\mathrm{C}=32\\ \mathrm{conv},1\times 1,256\end{array}\right]\times 3\) \\ \hline \(28\times 28\) & \(\left[\begin{array}{c}\mathrm{conv},1\times 1,128\\ \mathrm{conv},3\times 3,128\\ \mathrm{conv},1\times 1,512\end{array}\right]\times 4\) & \(\left[\begin{array}{c}\mathrm{conv},1\times 1,256\\ \mathrm{conv},3\times 3,256\mathrm{C}=32\\ \mathrm{conv},1\times 1,512\end{array}\right]\times 4\) \\ \hline \(14\times 14\) & \(\left[\begin{array}{c}\mathrm{conv},1\times 1,256\\ \mathrm{conv},3\times 3,256\\ \mathrm{conv},1\times 1,1024\end{array}\right]\times 6\) & \(\left[\begin{array}{c}\mathrm{conv},1\times 1,512\\ \mathrm{conv},3\times 3,512\mathrm{C}=32\\ \mathrm{conv},1\times 1,1024\end{array}\right]\times 6\) \\ \hline \(7\times 7\) & \(\left[\begin{array}{c}\mathrm{conv},1\times 1,512\\ \mathrm{conv},3\times 3,512\\ \mathrm{conv},1\times 1,2048\end{array}\right]\times 3\) & \(\left[\begin{array}{c}\mathrm{conv},1\times 1,1024\\ \mathrm{conv},3\times 3,1024\mathrm{C}=32\\ \mathrm{conv},1\times 1,2048\end{array}\right]\times\) \\ \hline \(3\) & \(1\times 1\) & Global average pool, 1000-d fc, softmax \\ \hline \hline \end{tabular}
\end{table}
Table 1: Configuration of the network structure (adapted from a previous study [18]).  
Figure 4: Structure diagram of SE-ResNeXt block.  
The numbers following the _fc_ indicate _C/r_ and \(C\), respectively. In Table 1, all basic modules of two network structures consist of three convolution layers with 16 modules. The first and the last are \(1\times 1\) convolution layers and the middle one is a \(3\times 3\) convolution layer. The difference between the two network structures is that a convolution group rather than a single convolution block exists in the SE-ResNeXt-50 module. Additionally, each module has a full connection operation. This paper will evaluate the SE-ResNeXt model based on the above network structure in the following section.