## 1 Introduction  
In tunnel engineering, the lining plays a critical role in withstanding the surrounding rock pressure and its own weight to maintain tunnel stability [1]. A lining thickness thinner than the design specification may pose potential risks to the tunnel structure [2]. Compared to traditional methods such as sampling and impact testing, GPR technology has gained widespread application in tunnel lining inspection due to its advantages of being non-destructive and efficient [3]. However, since the acquired GPR data consists of reflected electromagnetic waves from the underground environment rather than direct imaging of the tunnel lining structure, extensive data processing is required to characterize the internal structure [4]. Therefore, there is a need for an automatic recognition algorithm to enhance the interpretability of GPR data. With the advent of convolutional neural networks (CNNs), deep learning techniques have made significant advances in the detection of secondary tunnel lining thickness. CNNs possess the capability to learn high-level image features, enabling the automated interpretation of GPR images. Among these, the R-CNN and YOLO series are commonly employed for tunnel lining detection. In 2021, [PERSON] et al. proposed a deep learning-based automatic recognition method that integrates Mask R-CNN with the finite-difference time-domain (FDTD) method and DCGAN-based data augmentation. The model was trained on 28,600 synthetic and 118 real GPR images, achieving the automated identification of tunnel lining elements. Due to the significant discrepancy between the reflection signals in GPR images and actual geometric features, the method did not explicitly delineate structural contours. Instead, bounding boxes were used to annotate the approximate positions and extents of the targets [5]. In the same year, [PERSON] et al. developed a deep learning-based non-destructive evaluation method that integrates GPR and electromagnetic induction (EMI) data. Using YOLOv3 and a 1D CNN, the method achieved the high-precision automated estimation of rebar diameters and protective layer thicknesses [6]. In 2022, [PERSON] et al. proposed a method based on a Rotated Region Deformable Convolutional Neural Network (R2 DCNN), trained on both synthetic and real GPR datasets to detect internal defects and rebars in arbitrary orientations within tunnel linings. However, the model has not yet been validated on large-scale and diverse real-world datasets, and the current results may not fully reflect its generalizability across various practical scenarios [7]. That same year, [PERSON] et al. introduced a deep learning framework combining CNN and Vision Transformer (ViT) architectures, trained on 27,890 GPR images, to achieve the automatic classification of highway tunnel defects. However, its high computational requirements limit its potential for real-time monitoring on low-power edge devices [8]. In 2023, [PERSON] et al. proposed a deep learning framework based on SA-DenseCL that incorporates self-supervised learning to reduce dependence on annotated data. A limited set of labeled GPR images was used to fine-tune Mask R-CNN for estimating rebar distribution, void defect locations, and secondary lining thickness. However, its heavy computational burden during the pretraining phase restricts its deployment in resource-constrained environments [9]. That same year, [PERSON] et al. introduced an integrated method based on M-YOLACT, which employed a multi-task deep neural network along with curve-fitting post-processing to simultaneously identify tunnel lining defects and estimate both defect depth and lining thickness from GPR images [10]. In 2024, [PERSON] et al. developed a YOLOP-based algorithm and applied transfer learning using a dataset containing three types of underground targets: rebar in concrete, buried pipelines, and hidden defects behind shield tunnel linings. The model enabled the automatic detection of defects behind railway tunnel linings and estimation of their thickness [11].