## 2 Methods
### Introduction of Network Architecture
#### 2.2.2 Encoder-Decoder Network Architecture  
The monocular visual neural network proposed in this paper consists of an encoder-decoder deep neural network. The network model is composed of four modules: encoder, decoder, feature fusion module and refinement modules, as shown in Figure 6.  
Taking the macroscopic texture depth map of asphalt as the supervisory signal, the RGB image is directly input into the CNN model, and the network structure is shown in Table 2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number** & **Block Name** & **Input/C** & **Output/C** & **Output/Size** \\ \hline \multirow{3}{*}{\(\times 3\)} & Conv1 & 3 & 64 & 104 \(\times\) 72 \\  & Residual Block 1 & 64 & 256 & 52 \(\times\) 36 \\  & Residual Block 2 & 256 & 512 & 26 \(\times\) 18 \\  & Residual Block 3 & 512 & 1024 & 13 \(\times\) 9 \\  & Residual Block 4 & 1024 & 2048 & 13 \(\times\) 9 \\  & Conv2 & 2048 & 1024 & 13 \(\times\) 9 \\  & Up1 & 1024 & 512 & 26 \(\times\) 18 \\  & Up2 & 512 & 256 & 52 \(\times\) 36 \\  & Up3 & 256 & 128 & 104 \(\times\) 72 \\  & Up4 & 128 & 64 & 208 \(\times\) 144 \\  & Up5 & 256 & 16 & 208 \(\times\) 144 \\  & Up6 & 512 & 16 & 208 \(\times\) 144 \\  & Up7 & 1024 & 16 & 208 \(\times\) 144 \\  & Up8 & 2048 & 16 & 208 \(\times\) 144 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The size of the output characteristics and the input/output channels of each layer. Figure 6: Network model. The encoder uses an improved ResNet101 as the feature extractor to downsample the input image, extract details and multi-scale features at different resolutions, and pass the feature map output by the final downsampling module to the decoder. The encoder consists of 33 residual blocks, each including convolutional layers with sizes of \(1\times 1\), \(3\times 3\), and \(1\times 1\). It employs skip connections to link the input feature signal with the output feature after convolution. This method preserves the original features while incorporating deeper, more complex features. The residual structure is shown in Figure 6(a). The encoder of the proposed CNN differs from ResNet101 in several ways. Specifically, the \(3\times 3\) convolutional layers in the last residual block (Residual Block 4) is replaced with a dilated convolution layer with a dilation rate of 2, which increases the resolution of the output feature map from \(1/32\) of the input image to \(1/16\). Additionally, the final average pooling layer, fully connected layer, and SoftMax layer are removed, and Residual Block 4 is directly connected to the decoder. The decoder consists of one convolutional layer and four up-sampling layers, labeled Conv 2, Up1, Up2, Up3 and Up4, respectively. The feature map doubles in size with each upsampling operation. After four upsampling steps, the output feature map has dimensions of \(208\times 144\times 64\). Using a standard encoder-decoder network typically results in the loss of complex texture information [20]. To address this, a refinement module is introduced in this paper. First, edge information from the 2D image is extracted using the Sobel operator. Next, a \(3\times 3\) convolutional layer (Conv 4) is used to embed the edge features of the pavement texture image into the refinement module. Finally, these edge features are integrated with the feature map produced by the feature fusion layer and the decoder through the Conv 5, Conv 6, and Conv 7 convolutional layers. The final depth map is then output. The feature fusion module includes four upsampling layers: Up5, Up6, Up7, and Up8. The feature maps at four different scales, generated by the encoder's downsampling, are upsampled by factors of 2, 4, 8, and 16, respectively. Each output feature map has dimensions of \(208\times 144\times 16\). Channel transformation is then applied via convolutional layers to produce the output of the feature fusion layer.