## 4 Ablation Study Analysis
Table 1 lists the secondary lining thickness estimation results of four model groups along Survey Line A in the Longhai tunnel section from D9 + 015.195 to D9 + 065.195. The \"Signal-Based Thickness\" refers to the signal recognition output obtained using the peak amplitude criterion described earlier. Table 2 presents the relative errors between the image-derived thickness and the signal-based thickness. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Track No.**} & \multirow{2}{*}{**Length (m)**} & \multirow{2}{*}{\begin{tabular}{c} **Signal-Based** \\ **Thickness** \\ **(mm)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **SD of Signal-** \\ **Based** \\ **Thickness** \\ **(mm)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Group A** \\ **(mm)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **SD of A** \\ **(mm)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Group B** \\ **(mm)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **SD of B** \\ **(mm)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Group C** \\ **(mm)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **SD of C** \\ **(mm)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Group D** \\ **(mm)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **SD of D** \\ **(mm)** \\ \end{tabular} } \\ \hline
1–1000 & 10 & 591 & 44.3 & 564 & 88.3 & 593 & 62.6 & 549 & 89.7 & 588 & 31.5 \\
1001–2000 & 10 & 558 & 48.2 & 679 & 100.4 & 595 & 69.8 & 512 & 74.8 & 544 & 38.5 \\
2001–3000 & 10 & 586 & 40.8 & 549 & 96.6 & 618 & 28.8 & 538 & 88.6 & 563 & 35.2 \\
3001–4000 & 10 & 580 & 43.3 & 573 & 98.5 & 609 & 37.5 & 579 & 82.9 & 596 & 32.6 \\
4001–5000 & 10 & 592 & 22.3 & 589 & 98.6 & 611 & 45.4 & 587 & 84.6 & 614 & 19.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Estimated thickness and standard deviation of the secondary tunnel lining. Figure 6: Output results of the ablation study models. From left to right: input GPR image, signal recognition result based on the peak amplitude criterion, and output results of Models A, B, C, and D.  
The IoU values of models A, B, C, and D were 0.779, 0.828, 0.792, and 0.841, respectively. The maximum thickness deviations between the predicted results and the signal-based results were 121 mm, 37 mm, 48 mm, and 23 mm, respectively. With the introduction of the transfer learning strategy, the average relative error between the predicted thickness and the signal-based results was reduced by 2.73%. Based on this, the further integration of the CBAM attention mechanism led to an additional 4.17% reduction compared to the baseline model. In addition, models A, B, and C exhibited significantly higher spatial standard deviations in thickness, possibly due to incomplete identification of secondary lining regions in certain parts of the GPR images. In terms of inference efficiency, the average processing time per image was 27.4 ms and 37.7 ms for models A and B, and 36.7 ms and 28.6 ms for models C and D, respectively. These ablation study results demonstrate that the proposed improved model achieves higher accuracy and stability in recognizing secondary tunnel lining regions, while introducing only a minimal increase in computational cost.