## 4 Ablation Study Analysis  
The model training in this study was conducted using PyTorch 2.3.0, with the stochastic gradient descent (SGD) optimizer for weight updates. The weight decay was set to \(5\times 10^{-4}\), the momentum to 0.937, and the initial learning rate to \(1\times 10^{-2}\). To evaluate the impact of transfer learning and the CBAM attention mechanism on model performance, four groups of comparative experiments were designed. Group A used the YOLOv8-seg architecture, which was randomly initialized and trained for 200 epochs on the mixed GPR dataset. Group B adopted the same architecture as Group A, with 100 epochs of pretraining on the COCO dataset followed by 100 epochs of fine-tuning on the mixed GPR dataset. Group C employed the improved architecture with the CBAM attention module, trained from scratch on the mixed GPR dataset for 200 epochs. Group D shared the same architecture as Group C, but its backbone was initialized with pretrained weights from Group B and trained on the mixed GPR dataset for 100 epochs. In terms of computational complexity, the model without CBAM had approximately 110.0 GFLOPs, which slightly increased to 110.2 GFLOPs after CBAM was introduced, indicating minimal overhead. All experiments were conducted on a hardware platform equipped with an NVIDIA RTX 4060 Laptop GPU and an Intel i7-13650 HX CPU. As shown in Figure 6, the recognition results of the four model groups are compared. The first column displays the input GPR images, while the second column visualizes the signal recognition output obtained using the peak amplitude criterion described in [13]. The third to sixth columns show the outputs of Groups A to D, with the secondary tunnel lining regions overlaid in red masks. All input GPR images were generated from 1000 scan traces, corresponding to a maximum two-way travel time of 25 ns. As observed, the output of Group A failed to identify certain secondary lining regions near the image boundaries and mistakenly classified non-lining areas as secondary lining. In Group B, the number of undetected secondary lining regions near the boundaries was noticeably reduced. Similar to Group A, the Group C output also failed to detect some secondary lining regions near the image edges and misclassified some non-lining areas. However, the extent of misclassification was lower than that of Group A. Group D further reduced the number of undetected secondary lining regions near the boundaries. Figure 5: Schematic diagram of the improved module architecture. The left panel shows the structure of the modified C2 fCBAM module, while the right panel illustrates the CBAM-Bottleneck structure used within the C2 fCBAM module. To estimate the thickness of the secondary lining based on the model's segmentation output, the relative error of the image-derived thickness was used as a evaluation metric. The image-derived thickness refers to the estimated lining thickness obtained after pixel-level segmentation of the secondary lining region in GPR images using a deep learning model, which is calculated based on the mapping between image pixels and physical distance. The thickness and its relative error were computed using the following equations:  
\[H=\frac{C}{\sqrt{\varepsilon}}\frac{t}{2} \tag{3}\]  
\[R=\left(\frac{|H_{img}-H_{sig}|}{H_{sig}}\right)\times 100\% \tag{4}\]  
where \(C\) is the speed of light in vacuum and is set to 0.300 m/ns, \(\varepsilon\) is the relative permittivity of the medium, and \(t\) is the two-way travel time of the electromagnetic wave through the secondary tunnel lining, measured in nanoseconds (ns). The two-way travel time can be converted based on the mapping between the maximum travel time set during field measurements and the image height in pixels. \(R\) represents the calculated relative error, \(H_{img}\) and \(H_{sig}\) refer to the thickness estimated by the image recognition and the thickness obtained using the signal-based recognition method, respectively, both measured in millimeters (mm).