# Recognition of Earthquake Surface Ruptures Using Deep Learning
## 3 Methods  
### Deep Learning Network Model  
After preparing the sample data, a suitable deep learning network must be built for identifying surface cracks in high-resolution UAV images. The purpose is to identify tiny linear targets, indicating surface cracks, using the trained deep learning model in the UAV image. Therefore, the receptive field should be increased to obtain more image features and combine multi-scale information to achieve better segmentation results when designing the network. A good network structure can achieve qualified results with a small number of samples if we balance the cost of manual interpretation of the samples and the timeliness of the post-earthquake disaster assessment. This study selected the 16-layer visual geometry group (VGG16) network as the primary network architecture. Then, we improved and optimized the network, constructing a deep learning semantic segmentation network that can directly realize the end-to-end recognition of surface cracks in high-resolution UAV images. The VGG network series is a very mature network structure that has shown outstanding results in image classification and object detection tasks [27]. We improved the VGG16 network and deleted several convolutional layers to reduce computation and memory consumption. Moreover, we added dilated convolution and spatial pyramid pooling (ASPP) to make the network perform well in surface crack identification. The network architecture is shown in Figure 4. The comparison of the number of parameters and Giga Floating Point Operations (GFLOPs) is shown in Table 1. After the improvement, our network only contained about 2.54 million parameters and performed 6.4 GFLOPs. Figure 4: Network architecture. The following improvements were made based on the VGG16 network architecture. First, the network was transformed into an FCN to achieve end-to-end semantic segmentation. Then, the step of the 3 \(\times\) 3 convolution kernel was set as 1 in the front part of the entire network. The next convolutional layer consisted of convolution kernels with a size of 1 \(\times\) 1 and a step of 1. The filter size of the pooling layer was 2 \(\times\) 2, and the step of the Pool1 layer was 2. However, to achieve dilated convolution, we set layers Pool2 and Pool3 (step of 1) and adopt the rectified linear activation function (ReLU). The first and second layers of the entire network still used standard convolution. For a larger receptive field in the extracted feature map, the third convolutional layer used dilated convolution. In addition, to alleviate the grid effect caused by dilated convolution, the network adopted a hybrid dilated convolution (HDC) design [28]. To identify small and large objects in the crack identification process, we designed the dilation rate to increase from low to high and follow the rules proposed by [PERSON] et al. [28]. Therefore, we set dilation rates of [1,3,5] for each layer. Then, to implement an FCN, we applied the convolution kernels with a size of 1 \(\times\) 1 and convolutions with a step size of 1 for the fourth, fifth, and sixth layers of the original fully connected layer. We used an atrous spatial pyramid pooling (ASPP) module to achieve multi-scale fusion and obtain more information [29] (Figure 5). In the FC4 layer, four sets of different dilated convolutions with dilation rates of 6, 12, 18, and 24 were used, respectively, the FC5 layers and FC6 layers were connected, and the fusion of pixel addition was performed. Finally, the SoftMax function was applied to obtain the predicted probability label map. Good results can be obtained by direct bilinear upsampling on the probability map. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Parameters (Million)** & **GFLOPs** \\ \hline AlexNet & 60.97 & 0.7 \\ \hline VGG-16 & 138.36 & 15.5 \\ \hline VGG-19 & 143.67 & 19.6 \\ \hline ResNet50 & 25.61 & 3.9 \\ \hline ResNet101 & 44.66 & 7.6 \\ \hline Our Net & 2.54 & 6.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters and GFLOPs for common models and our network.