## 3 Methods
### Crack Recognition Model  
Convolutional neural networks can be used to build a deeper network with a smaller number of parameters, thus reducing the time and computational cost of the training process. VGG16, as a classical convolutional neural network architecture, consists of 13 conventional layers and 3 fully connected layers. It employs small 3 \(\times\) 3 convolutional kernels and incorporates maximum pooling layers, presenting a relatively streamlined yet deep architectural design, as shown in Figure 3. Consequently, in our crack recognition model, VGG16 was chosen as the classifier to convert the crack detection problem into a binary classification problem and classify the image samples with cracks and no cracks in the image data set. Figure 2: PS-InSAR technology flow chart. Viewing the VGG16 model as a classifier allows us to formalize the crack detection problem as a binary classification task, distinguishing between crack and non-crack image samples. Constructing a crack recognition model using VGG16 primarily involves three steps: data collection, data preprocessing, and model training. The first two steps have already been completed in the previous sections. There are two types of data: the data collected by dispatching drones based on the settlement value, and the second type is specially prepared for model training to improve the universality of the crack recognition model. The size of the existing data set is \(227\times 227\), so the input structure of the VGG16 model is adjusted to \(227\times 227\). In order not to miss the boundary information, 0 is added to the periphery of the original image matrix. After the convolution operation, the pixel value will be reduced accordingly, making the resulting matrix smaller than the original matrix. Padding can solve this problem well and obtain the matrix by convolution consistent with the original matrix. Since it is a grayscale image, it is a \(3\times 3\) single-channel convolution kernel. The 64 convolution kernels are convolved to generate a 64-channel feature map. The activation function ReLU makes the output content non-linear. In addition, 64-channel \(3\times 3\) convolution kernels generate 64-channel feature maps, and the number of channels in the output feature map always equals the number of convolution kernels. During the training, the initial learning rate was set to 0.001, the SGD optimizer was used, and as for other parameters, momentum was set to 0.5. In order to further reduce the parameters, after the maximum pooling, average pooling can be performed. The uniform output size is \(7\times 7\times 512\) feature images. After flattening, there are 25,088 parameters (\(7\times 7\times 512\)), and the last three layers are fully connected. After the 13-layer convolution operation, the output of the last convolution layer is flattened and then input into the fully connected layer. The fully connected layer can map 25,088 parameters to 4096 neurons. Finally, the SoftMax layer is used to determine whether the input image contains cracks. Model codes are available in the Crack Recognition Model for Supplementary Materials. The training results of the model are shown in Figure 4. The convolutional network inputs 8206 images, 6566 images in the training set, and 820 images in the validation set. A batch-size has 64 images, and an echo has a total of 103 batch-sizes. All the data of training set are used for training, and epochs are set to 20, 30, 40, 50, 70, 80, 90 and 100 to find the best trained model. In this study, training loss, validation loss, training accuracy, and validation accuracy are utilized to calculate the accuracy of crack recognition model. When training for 20 epochs, as shown in Figure 4a, there is no convergence trend for training  
Figure 3: VGG16 model. accuracy and verification accuracy. At the 80 th epoch, as shown in Figure 4b, the training accuracy has not yet converged but the validation accuracy is close to convergence.