## 5 Experiments and Results
### Training Results of the Subway Tunnel Lining Apparent-Defect Detection Model
The performance of each experimental group on the test set is shown in Table 3. The test results for apparent defects include mAP@50 values for cracks, leakage, exfoliation, and all other categories to evaluate model accuracy. Finally, weight parameter heatmaps were generated via model weight visualization for auxiliary analysis and validation to enhance the interpretability of the model optimization process. Figure 11: (**a**) Train loss and validation loss of the proposed model. (**b**) The validation mAP of the proposed model. As shown in the Figure 13, combining the results of the ablation experiments with the weight heatmap analysis, it can be concluded that adding high-resolution detection layers significantly improves defect detection accuracy. The weight distribution results show that it can better utilize shallow texture features to enhance the focus on small target defects and more precisely fit the defect morphology, but the results also show some sensitivity to other detection targets and noise. Applying CA can improve the detection of defects and facilities in key areas by increasing the weight distribution for defect regions at the current feature layer scale, but it does not significantly enhance small target defect detection. Incorporating BoT3 enhances the weight distribution for medium- and large-scale defects, facility targets, and their background areas and is more sensitive to defect areas adjacent to tunnel surface facilities and other targets. Among the pairwise combinations of experimental groups, adding both high-resolution detection layers and CA simultaneously improves the parameter weights for small target defect areas and morphologies and frequently occurring defect areas (edges), providing better detection performance than other pairwise combinations. Ultimately, applying all three optimization strategies achieves finer granularity in the parameter weight distribution, better fits small target defect morphologies, and balances greater attention to edge regions where defects frequently occur and adjacent areas of tunnel surface facilities. This situation aligns more  
Figure 12: Train and validation results of the ablation experiment. (**a**) The mAP@50 result, (**b**) the mAP@50–95 result, (**c**) the precision result, (**d**) the recall result. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & **CA** & **HR Head** & **BoT3** & **Facilities** & **Bolt Hole** & **Joint** & **Crack** & **Exfoliation** & **Leakage** & **[EMAIL_ADDRESS]/Defects** & **[EMAIL_ADDRESS]/All** \\ \hline \multirow{5}{*}{Yolov\%**88**} & \(\times\) & \(\times\) & 0.92 & 0.982 & 0.756 & 0.708 & 0.768 & 0.829 & 0.768 & 0.827 \\  & \(\times\) & \(\  
closely with the spatial characteristics of apparent defects in tunnels. Compared with the YOLOv8s model, the optimized model achieved a mAP of 0.838 for the detection of three types of apparent defects, an improvement of 7%, and a mAP of 0.87 for all target categories in metro tunnels, an improvement of 4.3%. Thus, the optimized model is better suited for metro tunnel detection scenarios and is more sensitive to apparent defects in metro tunnels, validating the effectiveness of the optimization strategies.