## 2 Deep Learning Based Underground Pipeline Target Recognition Using 3-Dimensional GPR Data
### Data Preprocessing  
The pre-processing of GPR data can be divided into two steps: Direct wave suppression and denoising.  
For direct wave suppression, there are many methods, such as a mean filter, SVD, time threshold interception, and so on.  
The mean filter method effectively suppresses the direct wave by subtracting the pixel value of each row from the mean value of that row, and the results are shown in Figure 6b,f. For the echo of the longitudinal pipeline, the hyperbolic curve is more obvious after suppressing the direct wave, but for the transverse pipeline, because its echo signal shows a horizontal straight line, the signal characteristics of the transverse pipeline are also filtered out while suppressing the direct wave. Therefore, it is inefficient to use the mean filter method to suppress the direct wave for the transverse pipeline.  
The SVD method is often used to process the coherent signal, and the direct wave in the GPR B-Scan data has the largest energy and strong correlation, so the direct wave can be suppressed by the SVD method, and the results are shown in Figure 6c,g. However, while suppressing the direct waves in the transverse pipeline echo signal, it will also weaken the signal characteristics of the transverse pipeline. Therefore, the effect of suppressing the direct waves by the SVD method is also unsatisfactory.  
The time threshold interception method cuts off the part of the echo data containing the direct wave directly, which does not weaken the signal characteristics of the transverse line pipeline and retains the characteristics of the hyperbola well. The results are shown in Figure 6d,h. Therefore, we choose the time threshold interception method to suppress the direct wave in this paper.  
Figure 5: Schematic diagram of bilinear interpolation.  
After suppressing the direct waves, there is still noise in the data, so the denoising process is still needed. In this paper, we adopt a simple and effective method based on wavelet decomposition, called the WTD method. Its specific process is shown in Figure 7. In this paper, we choose the wavelet transform using the db4 wavelet base, and the threshold value is determined according to the specific situation. Its denoising result is shown in Figure 8.  
After data preprocessing, the dataset, including the transverse pipeline, the longitudinal pipeline, the underground cavity, and no target, is shown in Table 3.  
Figure 8: The WTD denoising result. (**a**) The image after suppressing the direct wave. (**b**) The image after WTD denoising.  
Figure 6: The results of suppressing direct wave. (**a**) The original longitudinal pipeline. (**b**) The result of mean filter method on (**a**). (**c**) The result of SVD method on (**a**). (**d**) The result of time threshold interception method on (**a**). (**e**) The original transverse pipeline. (**f**) The result of mean filter method on (**e**). (**g**) The result of SVD method on (**e**). (**h**) The result of threshold interception method on (**e**).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classification Categories** & **Simulation Data** & **Real-World Data** \\ \hline Longitudinal pipeline & 370 & 111 \\ Transverse pipeline & 557 & 127 \\ Cavity & 315 & 97 \\ No target & 423 & 83 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Composition of the dataset after data preprocessing.  
Figure 7: The WTD denoising process.