## 6 Conclusions  
This study proposes a YOLOv8-seg-based model for secondary tunnel lining recognition and thickness estimation. The model leverages a deep convolutional neural network to perform pixel-level segmentation of secondary lining regions in GPR images and estimate their average thickness. To enhance feature extraction under complex backgrounds, the model incorporates the CBAM attention mechanism, which combines channel and spatial attention to strengthen the response to key interface features. In addition, a transfer learning strategy is applied, where the model is pretrained on the COCO dataset and fine-tuned using both field-measured and numerically simulated data. This approach improves the convergence speed and recognition accuracy under small-sample conditions and enhances the model's generalization in both architectural design and training strategy. The experimental results demonstrate that the proposed model can reliably identify secondary lining regions in the field dataset collected from the Longhai tunnel. For the section from D9 + 015.195 to D9 + 065.195, the average relative error compared to the signal-based peak detection method was 2.68%. The thickness estimation deviation remained within 25 mm, showing high Intersection over Union (IoU) and low average relative error.  
This study still has several limitations. The model relies heavily on representative simulated data and high-quality annotated field data during training, and limitations in data diversity or annotation accuracy may affect its generalization capability. The current model evaluation is limited to a single engineering scenario, and its stability under varying tunnel types, geological conditions, and complex interference environments remains to be validated. In addition, thickness estimation is still based solely on image segmentation, lacking joint modeling with time-domain signals, which may limit prediction accuracy. Future research could expand data sources and integrate raw GPR signals with image information to develop a multimodal model, and incorporate uncertainty estimation mechanisms to enhance model robustness and interpretability in complex scenarios.  
Conceptualization, C.L. and T.P.; methodology, C.L., T.P. and N.C.; software, T.P.; validation, X.Y. and H.L.; formal analysis, T.P.; investigation, X.Y.; data curation, L.W., H.L. and X.Y.; writing--original draft preparation, T.P.; writing--review and editing, C.L.; project administration, C.L. and L.W.; funding acquisition, C.L. All authors have read and agreed to the published version of the manuscript.  
This work is funded by the National Natural Science Foundation of China (Grant No. 62263015), and supported by the Yunnan Provincial Key R&D Program (Grant NO. 202203 AA080006).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
Authors [PERSON] and [PERSON] were employed by the company Yunnan Aerospace Engineering Geophysical Detecting Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.