## 3 Method
### Convolution Module  
In the initial network design, convolutional layers were constructed to separate each input channel, with multiple small kernels convolving the data channel-wise. However, depthwise convolution alone is insufficient to expand the feature space for subsequent operations [40; 41; 42]. Therefore, a high-dimensional point convolution kernel of size 1*1 is introduced after the depthwise convolution, and the multiple mapping features after the depthwise convolution are fused and transformed into high-dimensional mapping features, while the relationship between the mapping features of each channel is established, which finally forms the convolution calculation process as shown in Figure 2. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network Layer** & **Input Data** & **Kernel Parameters** & **Step** \\ \hline Input layer IN & (3, 1024, 1024) & nil & nil \\ Convolution layer A & (3, 1024, 1024) & (32, 3, 3, 3) & 2 \\ Inverted residual layer B & (32, 512, 512) & \(\begin{bmatrix}(32, 32, 1, 1)\\ (32, 32, 3, 3)\\ (16, 32, 1, 1)\\ (96, 16, 1, 1)\\ (96, 96, 3, 3)\\ (24, 96, 1, 1)\\ \hline Inverted residual layer D & (24, 256, 256) & \(\begin{bmatrix}(144, 24, 1, 1)\\ (144, 144, 3, 3)\\ (32, 144, 1, 1)\\ \end{bmatrix}*3\) & \(\begin{bmatrix}2\\ 1\\ 1\\ 2\\ 1\\ \end{bmatrix}\) \\ Inverted residual layer E & (32, 128, 128) & \(\begin{bmatrix}(192, 24, 1, 1)\\ (192, 192, 3, 3)\\ (64, 192, 1, 1)\\ \end{bmatrix}*4\) & \(\begin{bmatrix}(192, 24, 1, 1)\\ (192, 192, 3, 3)\\ (64, 192, 1, 1)\\ \hline Inverted residual layer F & (64, 64, 64) & \(\begin{bmatrix}(384, 64, 1, 1)\\ (384, 384, 3, 3)\\ (128, 384, 1, 1)\\ (576, 128, 1, 1)\\ (576, 576, 3, 3)\\ (256, 576, 1, 1)\\ (960, 256, 1, 1)\\ (960, 960, 3, 3)\\ (512, 960, 1, 1)\\ \hline Parallel pooling layer I + J & (512, 64, 64) & \(\begin{bmatrix}(512, 512, 64, 64)\\ (512, 512, 32, 32)\\ (512, 512, 22, 22)\\ (512, 512, 11, 11)\\ \end{bmatrix}\) \\ Feature fusion layer K & \(\begin{bmatrix}(512, 64, 64)\\ (512, 64, 64)\\ (512, 64, 64)\\ (512, 64, 64)\\ (512, 64, 64)\\ (512, 64, 64)\\ (512, 64, 64)\\ (512, 64, 64)\\ (512, 64, 64)\\ (128, 64, 64)\\ Up-sampling layer N & (2, 64, 64) \\ Output layer OUT & (2, 1024, 1024) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overall network structure. This method reduces the number of parameter factors compared to the direct use of high-dimensional large convolutional kernels, thus reducing the amount of operations. However, due to the decreasing number of parameter factors, the fitting ability of individual network layers also decreases relatively, and the network needs to be built by combining methods such as classical convolutional layers and deepening network layers to get good output. Therefore, the lightweight optimization of the residual module is combined with the residual module of ResNet and MobileNetV2. Therefore, combining the residual module of ResNet and MobileNetV2's lightweight optimization of the residual module [34], the full convolutional layer is first used to increase the data dimensionality, and then different convolutional steps and inflated convolutional architectures are used to construct the inverted residual layer to complete the depthwise convolution and dimensionality change operations. The internal structure of the inverted residual layer is shown in Figure 3. Firstly, the channel number of feature mapping is increased by a 1*1 convolution kernel. Then, the convolution operation is carried out by a 3*3 convolution kernel. Subsequently, the channel number of feature mapping is either returned to the initial state or transformed to other dimensions using a 1*1 convolution kernel; combined with residuals, and finally, the output is completed. In the inverted residual layers G and H, the theory of dilated convolution [43] is introduced, which transforms depthwise convolution into depthwise dilated convolution.