## 2 PD Pattern Recognition Based on MCNN
### Mobilent's Convolution Neural Network  
With the continuous increase of the number of layers of convolution neural network, the number of parameters and the amount of calculation also increase. This leads to a complex model structure and slow computing speed, which is not conducive to the application and development of CNNs in the field of pattern recognition with real-time online requirements. To solve these problems, Google proposed the MobileNets convolution neural network (MCNN) in 2017 [17]. A Depthwise Separable Convolution structure is used in the unit block of the model, which greatly reduces the number of parameters and computation of the model and achieves more efficient performance through the reverse residual structure with a linear bottleneck. On this basis, we add the Squeeze-and-Excitation structure to the model used in the paper and adjust the activation function to further improve the learning ability and timeliness of the model [18].  
In the MobileNets model, the depth separable convolution is used instead of traditional standard convolution. Depth separable convolution mainly includes two parts: depth convolution and \(1\times 1\) point convolution [19]. This decomposition is linear, which can reduce the computation and model parameters, as shown in Figure 2.  
Figure 2a is the traditional standard convolution, the number of convolution kernels is N, and the number of input channels is M. The traditional standard convolution can be decomposed into the sum of the depth convolution shown in (b) and the point-by-point convolution shown in (c). The MobileNets can reduce the number of parameters and improve the computational efficiency by using the depth separable convolution structure.  
Theoretically, the recognition effect of the model with more layers of the network will be better. But practical experience shows that the difficulty of network training and optimization increases with the increase of layers, which leads to network degradation, and the effect will be worse than that of the relatively shallow network. In the traditional residual structure shown in Figure 3, the above problem is solved by introducing a shortcut connection structure. The dimension of the feature channel is first reduced to the lower dimension and extracted features, then extended to the high dimension. To improve the classification performance of the network and alleviate the problem of gradient vanishing  
Figure 2: Standard convolution and depth detachable convolution. (**a**) Standard convolution filters. (**b**) Depth convolution. (**c**) \(1\times 1\) convolution.  
in multi-layer backpropagation, the reverse residual structure with linear bottleneck is introduced into the MobileNets, as shown in Figure 4. The feature channel dimension in the reverse residual structure is first extended to the higher dimension to extract features and then reduced to the lower dimension. This structure can increase the nonlinear expression capacity of each feature channel by deep convolution in the high-dimensional feature space and maintain the compact feature expression in the input and output information, which makes the structure of the model more efficient.  
The feature transfer in the traditional convolution network is to transfer the weight of the feature graph to the next layer, and the Squeeze-and-Excitation (SE) module can adaptively correct the importance of features between channels according to the global loss function of the network, to increase the weight of effective features. This structure can strengthen the learning ability of the network and improve the accuracy of the model by strengthening the important features.