## 5 Comparative Experimental Analysis  
To further evaluate the performance of the improved model, three mainstream instance segmentation models--Mask R-CNN, YOLOv5-seg, and YOLACT--were selected as comparison baselines. Mask R-CNN integrates a Region Proposal Network (RPN) with a Fully Convolutional Network (FCN), offering strong multi-task capability and demonstrating excellent performance in fine-grained segmentation tasks. YOLOv5-seg adds a segmentation head to the YOLOv5 detection framework, enhancing its ability to detect multi-scale object boundaries in complex backgrounds. YOLACT employs a decoupled mask generation mechanism to achieve fast instance segmentation, balancing both object integrity and edge precision. To ensure fairness and consistency in comparison, all three models were pretrained for 100 epochs on the COCO dataset and then fine-tuned for another 100 epochs on the constructed mixed GPR dataset. The specific training settings were as follows: YOLOv5-seg used an initial learning rate of 0.01 with the SGD optimizer, and a linear learning rate decay strategy. Both YOLACT and Mask R-CNN used an initial learning rate of 0.001 with the Adam optimizer and adopted a step-based learning rate adjustment strategy. These configurations ensured a standardized training process and facilitated a fair evaluation of model performance. Figure 7 presents a comparison of the experimental results. The first column shows the input GPR images, and the second to fifth columns display the outputs generated by the four models. In the results of YOLOv5-seg and YOLACT, certain secondary lining regions near the image boundaries were not successfully detected. YOLOv5-seg also misclassified some primary lining areas as the secondary lining. Mask R-CNN failed to detect certain secondary lining regions located near the boundary between the secondary and primary lining \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Track Number**} & **Group A** & **Group B** & **Group C** & **Group D** \\  & **Restoration Rate** & **Restoration Rate** & **Restoration Rate** & **Restoration Rate** \\ \hline
1-1000 & 4.57\% & 0.34\% & 7.11\% & 0.51\% \\
1001-2000 & 21.68\% & 6.63\% & 8.24\% & 2.51\% \\
2001-3000 & 6.31\% & 5.46\% & 8.19\% & 3.92\% \\
3001-4000 & 1.21\% & 5.00\% & 0.17\% & 2.76\% \\
4001-5000 & 0.51\% & 3.21\% & 0.84\% & 3.72\% \\ Average Relative & 6.86\% & 4.13\% & 4.91\% & 2.68\% \\ Error & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Relative error of image-derived thickness estimation. To quantitatively compare the recognition results of the above models, Equations (3) and (4) were used to estimate the image-derived thickness and compute the relative error. Table 3 presents the thickness estimation results of the three models. Table 4 shows the relative errors between the model-estimated thickness and the signal-based thickness. The IoU values of the three baseline models were as follows: 0.742 for Mask R-CNN, 0.825 for YOLOv5-seg, and 0.804 for YOLACT. The corresponding maximum thickness deviations from the signal-based results were 65 mm, 38 mm, and 45 mm, respectively. The relative errors were 6.88%, 3.38%, and 4.44%, respectively. In addition, YOLOv5-seg and YOLACT exhibited significantly higher spatial standard deviations in thickness estimation, indicating possible omissions in identifying certain secondary lining regions. By contrast, the proposed improved model achieved a maximum deviation of less than 25 mm from the signal-based results in average secondary lining thickness estimation, with an average relative error of 2.68%, and an IoU value of 0.841, demonstrating superior performance in both thickness estimation accuracy and recognition stability.