## 2 Deep Learning Based Underground Pipeline Target Recognition Using 3-Dimensional GPR Data
### Real Data Acquisition and Simulation Data Generation  
Due to the complexity of the collection and verification process of real 3D underground pipeline data, it is difficult to obtain a large amount of data for training. Therefore, this paper adopts the method of mixing real-world data and simulation data. The real-world data in this paper come from the actual engineering project of Dalian Zoroy Company (Dalian, China), and the ZRY-100A vehicle-mounted 3-dimensional GPR equipment is used for data collection, whose principle is shown in Figure 3 and parameters are shown in Table 1. We conduct field road detection in many cities and analyze targets such as cavities and pipelines in GPR images. The parameters of most roads are shown in Table 2. Then we intercept and classify the targets based on the experience of engineers. Finally, the accuracy of classification marks is verified by drilling and excavation on site. In the field of GPR, GprMax is an open-source simulation software developed by a team from the University of Edinburgh in the UK, which has been updated to version 3.0. It uses the Finite Difference Time Domain (FDTD) to simulate electromagnetic wave propagation and solves Maxwell equations in three-dimensional space for forwarding simulation. It is widely used in the numerical modeling of GPR [32]. To improve the robustness of the deep learning model and expand the training set, this section uses GprMax software to generate 3-dimensional GPR echo images by setting different simulation parameters. In the paper, a 1.2 m \(\times\) 1.2 m \(\times\) 1.2 m underground space is selected for simulation to generate 3-dimensional GPR images. In terms of pipelines, they can be divided into transverse and longitudinal pipelines in the general direction. For these two kinds of \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Antenna frequency & 400 MHz \\ Pulse amplitude & 50–100 V \\ The number of channels & 36 \\ Channel gap & 0.1 m \\ Time window & 50 ns \\ The number of samples & 512 \\ Measurement point interval & 0.1 m \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The parameters of vehicle-mounted 3-dimensional GPR. Figure 3: Working principle diagram of vehicle-mounted 3-dimensional GPR. pipelines, the radius of the pipelines is taken as a random value in (0.02 m, 0.18 m), and the depth of the pipelines is also randomly generated as (0.3 m, 0.8 m). The medium of the pipelines can be selected as metal or PVC. In terms of cavities, spherical and cube-shaped cavities are generated randomly. The radius of spherical cavities is set as a random value in (0.05 m, 0.3 m), and their depth moves randomly within the range of (0.4 m, 0.7 m) in space. The cube void is set at the lower left of the cube starting position in (0.2 m, 0.6 m), whose length, width, and height are randomly within the range of (0.05 m, 0.3 m). Using GprMax3.0 for 3D modeling takes more time than 2D modeling and requires higher computer performance. In view of the low efficiency of manual single simulation, there is no need to wait each time to see the completion of the simulation process, and this paper realizes the batch generation of simulation data with the help of python script combined with the original GprMax3.0 package. We entered the type and number of.in files to be generated in the python script, randomly generated the batch.in files and saved them in the corresponding folders, then called the script for batch simulation, searched for.in files in the corresponding files, and called the script for batch simulation. We then called the script of batch simulation, searched for.in files in the corresponding files, called GprMax3.0 software package module orthogonal simulation to generate the corresponding output files and images and saved them, and then stored the data in the format of a 3D matrix with the help of Numpy.