## 4 Discussion and Conclusions  
In this paper, we proposed a method based on deep learning for underground pipe detection using 3-dimensional GPR images. First, this paper proposes a three-dimensional matrix rotation to augment the 3-dimensional GPR data and constructs the dataset, which may be the first dataset in the field of GPR, after data preprocessing. Second, this paper illustrates the advantages of the 3D-CNN compared with the 2D-CNN in underground pipe recognition. Furthermore, to solve the problems of excessive calculation and parameters in the 3D-CNN, this paper proposes a structure of depth-separable convolutional blocks with the dimensional fusion module. By adding this structure, the number of network parameters is reduced by 66.9% while its performance is similar to that of the 3D-CNN in the underground pipeline classification task, which provides a foundation for the future deployment of the model to mobile platforms with limited computing resources. Finally, this paper proposes a curve-fitting method based on the Canny operator to find the position of the pipe by detecting hyperbolic vertices in B-Scan and then finding the direction of the pipe in 3D space.  
There are still some shortcomings in the present study. For example, the real data are limited, and the simulation data cannot represent the real measurement results well. Different cities may have different image characteristics when the difference in local media is large. For future work, we will acquire more real-world data and build a more complete dataset where we can more accurately verify the performance of the network proposed in this paper. We will also acquire data from more cities to improve the generalization ability of the network. We also plan to find a suitable semi-supervised method to decrease the difficulty of manual annotation of 3-dimensional GPR images. In order to better prevent possible hazards and identify changes in the underground environment, we will try to propose an algorithm to compare the similarity of the data of the same section of highway  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Evaluation Indicators** & **2.5D-CNN** & **3D-CNN** & **DS-3D-CNN** & **DF-DS-3D-CNN** \\ \hline Accuracy & 0.9567 & 0.9976 & 0.9756 & **0.9880** \\ Precision & 0.9636 & 0.9975 & 0.9768 & **0.9898** \\ Recall & 0.9561 & 0.9561 & 0.9769 & **0.9886** \\ F1-Measure & 0.9585 & 0.9585 & 0.9767 & **0.9890** \\ parameters & 40,572 & 102,068 & 33,804 & **33,804** \\ Training time & 20.338 s & 104.797 s & 195.189 s & **254.886 s** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Evaluation index values for the classification performance of each algorithm.  
detected at different times and obtain the difference between them. Meanwhile, we will try to deploy the network to mobile platforms such as FPGA in the future to achieve real-time detection of underground targets.  
Methodology, X.B. and S.W.; Software, Y.Y., Z.W. and J.L.; Methodology, J.Z. and H.T.; Investigation, H.L. and G.L.; Writing--original draft, Z.W.; Writing--review & editing, Y.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under Grant 62071147.  
Not applicable.  
The data used in this article is not public.  
The authors declare no conflict of interest.