## 5 Experiments and Results  
### Training Results of the Subway Tunnel Lining Apparent-Defect Detection Model  
After setting up the experimental environment, the optimized model was trained and validated. The hyperparameters for the network model training phase were set as follows: stochastic gradient descent was used as the optimizer with an initial learning rate of 0.01, momentum of 0.937, a decay coefficient of 0.0005, a warmup epoch of 3, a batch size of 16, and a data loader worker count of 8. The training ran for 850 epochs. The training and validation losses are shown in Figure 10(a). During the first 50 epochs of model training, both the training loss and validation loss decreased rapidly, with the validation loss showing larger fluctuations. Subsequently, the rate of loss reduction slowed, and after 600 epochs, the training and validation losses stabilized, with the validation loss being lower than the training loss, indicating that the model did not overfit. Additionally, the mean average precision (mAP) values were recorded during each validation process (Figure 10(b)). The model's validation accuracy increased rapidly during the first 50 epochs. Then, the rate of increase slowed,  
Figure 10: Concrete crack image data and its degradation process. maintaining a low growth rate after 300 epochs and gradually converging after 600 epochs. The final model accuracy on the validation set stabilized at 0.879 and 0.652. Ablation Experiments on Detection Models of Apparent Defects in Subway Tunnel Linings and Related Facilities  
Ablation experiments were conducted to validate the effectiveness of the improved model and to investigate the impact of each introduced module on the model's detection performance. The experiments were divided into eight groups trained under the same experimental environment, training parameters, and dataset. Using YOLOv8s as the base model, CA mechanisms, BoT3 feature enhancement, and high-resolution detection layers were introduced at the corresponding positions in the model, and their combinations were tested to obtain experimental results. The accuracy changes during model validation for each experimental group are shown in Figure 12. In the initial stage of model validation, as no pretrained model was used, the initial inference had fewer predicted boxes, resulting in a lower recall value. However, the precision was higher, indicating that a high proportion of the few predicted boxes were correct. As the number of epochs increased, the number of predicted boxes gradually increased, the recall gradually increased, and the precision decreased, indicating a decrease in the proportion of correct predictions. Subsequently, both values gradually increased and eventually converged. The final improved model achieved a recall value of 0.831, the highest among the experimental groups, and a precision value of 0.915, the third highest among the experimental groups, representing a 1.9% improvement over the original model. Using mAP values to evaluate model accuracy, without any improvement strategies, the model achieved a mAP@50 of 0.831 and a mAP@50-95 of 0.604 for the detection of tunnel lining facilities and defects. During the first 100 epochs of model training, the validation accuracy was consistently greater than that of the other experimental groups. As the number of epochs increased, the model parameters further improved, and the validation accuracy of the other optimized experimental groups gradually exceeded that of the original YOLOv8s model. The applied optimization strategies showed progressive accuracy improvements. The final optimized model achieved mAP values of 0.879 and 0.652 for all categories during validation, representing a 4.8% improvement over the original model. This indicates that the optimized model can better detect tunnel lining facilities and defects during validation.