## 2 The Tunnel GPR Dataset and Transfer Learning Between Optical and Radar Images
Figure 1 illustrates the constructed simulation model of the tunnel secondary lining, the synthetic GPR image generated using gprMax, and the corresponding real GPR image. One of the key challenges in GPR image recognition is the requirement for large-scale datasets to effectively train the network and optimize weight parameters [14]. In contrast to natural photographs, generating meaningful GPR images is more challenging, as it demands additional time and manual effort. A widely adopted solution is transfer learning, which leverages large-scale optical image datasets such as ImageNet or COCO for network pretraining. Pretraining on such datasets can improve convergence during GPR training and enhance recognition accuracy. Transfer learning involves reusing pre-trained model parameters obtained from large-scale datasets. These parameters can be embedded into the backbone of the improved model and reused in the target domain. Although the COCO dataset primarily contains natural images with visual features that differ significantly from tunnel radar imagery, low-level statistical features learned from pixel values--such as histograms--can still provide significant benefits in the context of transfer learning [15]. Previous studies have demonstrated that shallow features obtained through transfer learning--such as edges, textures, and geometric patterns--retain strong generalization capabilities in target domains. For example, [PERSON] et al. validated the effectiveness of COCO-pretrained models in feature transfer for bridge radar image classification tasks, and noted that even when semantic differences exist between the source and target domains, low-level structural information remains transferable, contributing to faster convergence and improved accuracy in the target task [16]. This study employs model-based transfer learning, where the model is trained on the COCO dataset, and the knowledge gained from the convolutional neural network is transferred to the target domain. Subsequently, a new Neck network and Head network are created, with the transferred network model being integrated into a new convolutional neural network. The pretrained model is then fine-tuned end-to-end using GPR images to update its weights. The process of transfer learning is illustrated in Figure 2. To ensure the exceptional detection capabilities of the pretrained CNN model, a model pretrained on the COCO dataset is used as the starting point. This dataset encompasses 80 categories and over 200,000 images. After pretraining, the weight parameters of the model's backbone structure are extracted, and the task model is trained and fine-tuned using the custom radar image dataset. The transfer learning approach is an efficient method that minimizes computational resource requirements, accelerates convergence speed, reduces training time, enhances the training convergence of the GPR dataset, and minimizes the model's bias in estimating the average thickness of the secondary lining and signal recognition thickness. Figure 1: Schematic of the simulation model and corresponding GPR images. (**a**) Simulated tunnel secondary lining model; (**b**) GPR image generated using gprMax; (**c**) field-acquired GPR image.