## 3 Tunnel Secondary Lining Recognition and Thickness Estimation Model  
This study proposes a tunnel secondary lining recognition and thickness estimation model based on the YOLOv8-seg framework, leveraging the feature extraction capability of convolutional neural networks and the design of the segmentation head to perform pixel-level segmentation on each detected object, identifying the secondary lining area in the input GPR image. Figure 3 illustrates the deep learning model framework used for recognizing the tunnel secondary lining. The backbone network of the model is composed of standard convolution layers (Conv), the C2f module with cross stage partial connections (CSPs) [17], and spatial pyramid pooling fast (SPPF). The backbone network extracts the features of the in-phase axis wave groups associated with the tunnel lining layers in GPR images. The lower layers of the network primarily capture low-level features, such as edges and colors of the in-phase axis wave groups. The network subsequently extracts more complex texture features, followed by the detection of more complete contour and shape features of the in-phase axis wave groups, which possess distinguishable identifying elements. The feature fusion network, Neck, utilizes the feature pyramid network (FPN) [18] architecture for top-down sampling, transferring high-level semantic features downward to ensure that the lower-level feature maps contain richer tunnel lining features. Additionally, the path aggregation network (PAN) [19] structure is employed for bottom-up sampling, transmitting strong localization features from lower layers upwards to enhance the precise localization information in higher-level feature maps. The parameters are aggregated from different backbone layers to different detection layers, enhancing the representation of multi-scale features. Figure 3: Framework of tunnel secondary lining recognition and thickness estimation model based on YOLOv8-seg. Figure 2: Transfer learning between optical images and radar images. The Head network employs a Decoupled-Head detection head to divide the input feature layers of varying sizes into three branches for detection. The first branch performs a regression task after convolution, outputting a bounding box prediction for the tunnel secondary lining's location. The second branch performs a classification task after convolution, outputting the probabilities of the classes. The third branch performs a segmentation task after convolution, outputting a feature map used as the Mask coefficients. Additionally, a feature map of scale 52 x 52 is upsampled and output as the native segmentation feature map. By combining it with the Mask coefficients, the tunnel secondary lining segmentation result can be obtained after cropping and binarization. To improve the accuracy of secondary lining recognition in GPR images, we integrate the Convolutional Block Attention Module (CBAM) [20] into the YOLOv8-seg architecture. Specifically, a novel CBAM-Bottleneck module is constructed by embedding CBAM into the Bottleneck component, which is then integrated into the standard C2f module to form the enhanced C2 fCBAM structure. In the revised architecture, the final three C2f modules in the Neck are replaced with C2 fCBAM modules, enabling the attention-guided feature fusion of GPR images. This attention mechanism guides the model to focus more effectively on the two primary reflection horizons in GPR images by leveraging both channel and spatial attention pathways, thereby improving overall segmentation performance. CBAM is a lightweight attention mechanism designed for seamless integration into convolutional neural networks (CNNs), comprising a Channel Attention Module (CAM) and a Spatial Attention Module (SAM) that model the channel importance and spatial relevance, respectively. This method effectively highlights the secondary lining regions of the tunnel, suppresses irrelevant background features, and accentuates the spatial localization of key features within complex backgrounds.