## 4 Joint Extraction on Tunnel Faces Based on Image Segmentation Neural Network Models
### Joint Extraction of Tunnel Face Based on U-Net Deep Learning Architecture
#### 4.2.2 U-Net Convolutional Neural Network Parameter Selection  
The input image size of this convolutional neural network is 512 \(\times\) 512. After four down-sampling and four up-sampling processes, the output image size remains 512 \(\times\) 512, the same as the input. The U-Net uses a 3 \(\times\) 3 convolution kernel, ReLU activation function, and 2 \(\times\) 2 max pooling for down-sampling.  
(1) Convolution layer parameter selection  
The convolution kernel size is 3 \(\times\) 3, and its convolution processing principle is shown in Figure 20. The sliding step of the convolution kernel is 1. To ensure that the image size after convolution remains consistent with the original image, the original image needs to be padded with a value of 0. The number of output image channels depends on the number of convolution kernels in the convolution layer.  
Figure 19: U-Net convolutional neural network architecture.  
Figure 18: Dataset augmentation operations. (The orange line is added later to determine the direction of the picture).  
(2) Activation function selection  
In the U-Net convolutional network structure shown in Figure 19, the convolution layers indicated by blue arrows correspond to the Rectified Linear Unit (ReLU) activation function shown in Equation (9). Its corresponding function graph is shown in Figure 21.  
\[f(x)=\begin{cases}0,x\leq 0\\ x,x>0\end{cases} \tag{9}\]  
The ReLU function is chosen for several reasons. Firstly, it increases the non-linearity of the network, which is essential for learning complex patterns. Secondly, it improves the computational speed due to its simple mathematical operation. Lastly, unlike the sigmoid function, ReLU does not suffer from the vanishing gradient problem. The vanishing gradient problem occurs when gradients used for updating neural network weights diminish, making training ineffective. ReLU avoids this issue by allowing gradients to flow through the network without significant diminishment, making it particularly suitable for large-scale convolution operations. This justification highlights why ReLU is preferred in deep learning applications, particularly in convolutional neural networks (CNNs).  
(3) Pooling method selection  
Pooling is a down-sampling method that can reduce the image size and help prevent overfitting. There are two main types of pooling: max pooling and average pooling.  
The principle of max pooling is shown in Figure 22, where the maximum pixel value within the neighborhood is taken as the center pixel value.  
Figure 21: ReLU Function Graph.  
Figure 20: Schematic diagram of convolution processing.  
The principle of average pooling is shown in Figure 23, where the average pixel value within the neighborhood is taken as the center pixel value.  
This paper introduces the U-Net network to identify the structural information of the tunnel face. To maximize the distinction between structural information and background information, a 2 \(\times\) 2 max pooling method is used for image down-sampling.