## 3 Results
### Classification Results  
The classification performance of the proposed method and the other three control algorithms is compared using the generated training set. Under the condition that the hyperparameters remain the same, the comparison figure of the accuracy curve and loss function curve obtained by iteration is shown in Figure 23. The evaluation indexes of each model obtained through the test set are shown in Table 5. For an intuitive representation, the confusion matrix of true and predicted labels is represented as a heat map, and the four classifications (vertical pipeline, horizontal pipeline, void, and no target) are replaced by labels 0 to 3, respectively, as shown in Figure 24. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Classification Category**} & \multicolumn{2}{c}{**Longitudinal Pipeline**} & \multicolumn{2}{c}{**Transverse Pipeline**} & \multicolumn{2}{c}{**Cavity**} & \multicolumn{2}{c}{**No Target**} \\  & **Training** & **Testing** & **Training** & **Testing** & **Training** & **Testing** & **Training** & **Testing** \\ \hline number of 3D GPR images & 371 & 110 & 558 & 126 & 332 & 80 & 406 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Training set and test set of convolutional neural network. Figure 22: Schematic of hyperbolic vertex extraction in GPR B-Scan images. In Figure 24a, the classification performance of the traditional 2D-CNN is the weakest, and 18 of the 416 data samples are misclassified, while the 3D-CNN has the strongest classification performance, and only 1 sample is assigned to the wrong category. Therefore, the 3D-CNN has great strength in processing and analyzing 3-dimensional GPR data. However, compared with the 2D-CNN, the 3D-CNN requires more neural network parameters and a longer training time because of an extra dimension. In Table 6, the neural network parameters of the 3D-CNN are three times more than those of the 2D-CNN. When the network contains more fully connected layers, the difference in network parameters between the 3D-CNN and the 2D-CNN will be more significant. Figure 23: Classification performance analysis of each algorithm. (**a**) The accuracy rate of four classification algorithms with different epochs. (**b**) The loss function of four classification algorithms with different epochs. Figure 24: Classification heat map analysis of each algorithm. (**a**) Classification heat map of 2D-CNN. (**b**) Classification heat map of 3D-CNN. (**c**) Classification heat map of DS-3D-CNN. (**d**) Classification heat map of DF-DS-3D-CNN. In Figure 23, the DS-3D-CNN has worse convergence performance and accuracy than the 3D-CNN. However, the DS-3D-CNN has 33,804 parameters, which are significantly fewer than those of the 3D-CNN with 102,068 parameters by 66.9%. Although the reduction of the calculation amount also leads to a decline in accuracy, compared with the 2D-CNN, which has 40,572 parameters, the DS-3D-CNN has better accuracy performance and convergence performance with fewer parameters. The experimental results demonstrate the feasibility of replacing the original 2D neural network and 3D neural network with depth-wise separable convolution. The dimensional information will be separated while the DS-3D-CNN reduces the parameters. In contrast, the DF-DS-3D-CNN proposed in this paper increases the interactivity of channel dimension and space dimension using dimensional information fusion. Though the DF-DS-3D-CNN has the same number of parameters as the DS-3D-CNN and only increases the small amount of calculation brought by average pooling and SoftMax operation, it can efficiently improve the classification and recognition ability of the network model. The classification results illustrate that the algorithm can not only reduce the number of parameters by 66.9% compared with the 3D-CNN but can also maintain a recognition accuracy that differs from the 3D-CNN by less than 1%.