## 3 Results
### Model Testing of Field Data  
The field data collection was carried out at the test site in a geophysical environment at IFSTTAR, Nantes, France [34] to demonstrate the effect of our proposed network on the binary extraction of ROIs. The Gneiss 14/20 gravel region was selected as the experimental location. Three layers of 2.5-meter-long pipes were buried at different depths, serving as the main reflector for data collection. The actual buried depths of each pipe can be referenced in the as-built report of this geophysical test site. SIR 4000 control unit with GSSI common offset antenna of nominal center frequency at 400 MHz was equipped for radargram acquisition. Figure 13a showcases the radargram acquired by a 400 MHz GPR, featuring clearly presented hyperbolic patterns. The GPR field profile after preprocessing is shown in Figure 13b.  
Figure 12: ROI-binarized hyperbolic region extraction results from network architectures without a skip connection structure in complex scenarios.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Strategy** & **PSNR** & **SSIM** & **FSIM** \\ \hline Otsu threshold method & 55.7984 & 0.9917 & 0.8428 \\ \hline K-means clustering & & & \\ segmentation method & 58.0648 & 0.9953 & 0.9040 \\ \hline The Res-CNN method & 58.4759 & 0.9958 & 0.9677 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The results of three different quantitative evaluation methods in complex scenarios.  
In order to simplify the GPR field profile and separate the ROIs, the Otsu thresholding method, the K-means clustering segmentation method, and our proposed Res-CNN method are used to obtain the binarized results, and the ROI-binarized hyperbolic region results are shown in Figure 14a-c. Since the field data show strong airwaves at the ground surface, the three binarization methods can obtain good target extraction results in the ROIs after preprocessing. In Figure 14a, the Otsu thresholding method obtains a hyperbolic shape at the location of the peak with the strongest energy, and the multiple reflected waves in the GPR image also interfere with ROI-binarized hyperbolic region segmentation. In Figure 14b, the K-means clustering algorithm can achieve binarization of the target reflection region with peaks and troughs of reflected waves, but it is also strongly affected by multiple reflected waves. In Figure 14c, the proposed method demonstrates enhanced capability in separating the hyperbolic reflection ROIs for binarization. It achieves a cleaner background profile and effectively distinguishes the background distribution from the target reflection. Additionally, all three methods were affected by the results of airway pre-processing, resulting in airway clutter appearing on the surface. In future research, consideration will be given to employing more advanced clutter processing techniques to improve airway removal.  
Figure 13: Radargram from field data and its preprocessing result. (**a**) the field data results; (**b**) the preprocessing results.  
Figure 14: ROI-binarized hyperbolic region extraction results with different methods on the field data. (**a**) Otsu thresholding method with a threshold of 0.49. (**b**) K-means clustering segmentation method with 3 cluster centroid locations. (**c**) Our proposed Res-CNN method.  
The results of ROI binarization hyperbola region extraction for network structures with and without skip connections were also compared, as shown in Figure 15. The convolutional neural network with a skip connection structure can better achieve ROI-binarized hyperbolic region segmentation and characterization; hyperbolic morphology is more clean and complete in Figure 14c compared to the result in Figure 15.