## 3 Results
### Model Testing of Complex Scenarios  
To further test the applicability of the method for extracting reflection waves in complex environments, a more complex pipe model with porous media was designed as shown in Figure 9, which is based on the field data collection at the test site in geophysical environment at IFSTTAR, Nantes, France [34]. The model size is 5 m \(\times\) 13 m, and the top air layer is set to 0.1 m. The location of the excitation sources is shown by red stars. The permittivity and conductivity of the slope (gray) are 9 and 0.0001 S/m, respectively. In porous disturbance media, three layers of metal pipes with depths of 0.8 m, 1.5 m, and 2.2 m, respectively, with spacings of 0.5 m, 0.7 m, and 1 m, respectively, are buried. The radius of the pipes is 0.2 m.  
The forward simulation adopts the finite difference time domain algorithm, and the center frequency of the excitation source is 400 MHz. The special step of the forward model was set to 0.01 m in both the x and y directions. The time step was set to 0.023 ns, and the time window is 57.5 ns. The radargram obtained from the forward simulation result is shown in Figure 10a, and the GPR profile after preprocessing is shown in Figure 10b. Figure 10: _Cont._  
Figure 9: Complex underground pipe model distribution. In the forward simulation results, the strong energy from the airway and the interference of the background porous media can significantly affect ROI-binarized hyperbolic region. To reduce the effect of clutter on the target reflection, preprocessing methods are employed to enhance the quality of the image. As shown in Figure 10, the three layers of pipe reflection regions on the slope are shown in turn. The three pipes near the surface experience overlapping of the reflected waves due to the small spacing, and the bottom pipeline has a lager spacing and more clear reflected hyperbolic branching regions. To rapidly distinguish these ROIs from the background medium and simplify the GPR image to accentuate the characteristics of the reflected waves, three different segmentation methods are compared, as shown in Figure 11. A ground truth image is chosen as a reference, as depicted in Figure 11. The Otsu thresholding method is affected by noise and leaves many background noises in Figure 11, and the K-means clustering method suppresses the noise better, but the shape of the hyperbola is not clear enough in Figure 11. Our proposed network segmentation method can both have good suppression of noise and extract a complete hyperbolic morphology in Figure 11, showing that the algorithm has better robustness to complex reflections as well. Figure 11: \(Cont.\)  
Figure 10: Radargram from a more complex pipe model with porous media and its preprocessing result. (**a**) The forward simulation results; (**b**) the preprocessing results. We also measure the binarization results of the GPR images using three evaluation criteria, namely PSNR, SSIM, and FSIM, to avoid the subjectivity of visual inspection, and the quantitative results are given in Table 3. In terms of PSNR, the Res-CNN method performs the best, reaching 58.4759, surpassing the Otsu threshold method (55.7984) and the K-means clustering segmentation method (58.0648). In terms of SSIM and FSIM, the Res-CNN method also outperforms, achieving 0.9958 and 0.9677, respectively, while the  
Figure 11: ROI-binarized hyperbolic region extraction results with different methods. (**a**) Ground truth. (**b**) Otsu thresholding method with a threshold of 0.50. (**c**) K-means clustering segmentation method with 5 cluster centroid locations. (**d**) Our proposed Res-CNN method. performance of the Otsu threshold method and the K-means clustering segmentation method is slightly lower. Based on these metrics, the Res-CNN method exhibits superior performance in image segmentation, demonstrating higher accuracy and robustness.