## 4 Results  
### Model Training Results  
As shown in Figure 3, the YOLOv8 model showed good convergence and stability during the training process of the dataset for damage to Macau's gray brick walls in this study. The model's train loss reached 142.51, validation loss was 64.19, and mAP was 0.00 at the first epoch, reflecting that the model was still in the early stages of feature learning at the initial stage. As the training continued, the loss value dropped rapidly, and the validation loss dropped to a minimum of 2.68 at the 64 th epoch, indicating that the model's fit to the validation set was optimal. At the same time, the mean average precision (mAP) reached a peak of 0.71 at the 90 th epoch, indicating that the model's comprehensive performance in multi-class damage identification had reached a stable level. Finally, at the end of the 297 th epoch of training, the training loss dropped to a minimum of 1.40, indicating that the model's error on the training set was tiny and the training process tended to converge. After completing the training of the YOLOv8 model, this study selected the 64 th epoch (Model 1), the 90 th epoch (Model 2), and the 297 th epoch (Model 3) in the training process as representative models and compared and evaluated their detection performance on the test set to further verify the optimal performance timing and stage stability of the model in the task of identifying damage to gray brick walls (Figure 4). The mean average precision (mAP) of each model was 63.62%, 62.60%, and 61.51%, respectively, and the corresponding log-average miss rate (LAMR) also showed obvious differences in the category dimension. The model from the 64 th epoch had the highest overall mAP at 63.62%, showing it was the best at distinguishing different types of damage features during training. It scored 0.95 in the vandalism category, and its scores in the moss, missing, and damage categories were all above 0.70, which means it adapts well to damage types that have clear differences in texture, shape, and distribution. It obtained an AP value of 0.95 in the vandalism category, and the AP values in the moss, missing, and damage categories all exceeded 0.70, indicating that it has excellent adaptability to damage types, with obvious differences in texture, shape, and distribution characteristics. In contrast, the mAP of the 90 th epoch and 297 th epoch models slightly decreased to 62.60% and 61.51%, respectively. Even though the drop was small, the AP was much lower in harder categories to identify, like intact and stain, suggesting that the model might be overfitting or losing its ability to generalize features. Figure 3: Numerical statistics of the model training process. (Image source: drawn by the authors). According to the LAMR index, Model 1 shows a more balanced missed detection control ability in multiple categories. For example, in the moss and missing categories, its LAMR is 0.51 and 0.50, respectively, which is better than Model 2 (0.57 and 0.56) and Model 3 (0.51 and 0.60), indicating that the model is more robust for damage recognition in small-scale, high-overlapping areas. More significantly, the three models all show extremely low missed detection rates (all 0.00) in the vandalism category, further verifying that this type of damage is the most prominent in the training set and that the model can stably  
Figure 4: The mAP and LAMR statistical results: Model 1 (64 th epoch model), Model 2 (90 th epoch model), and Model 3 (297 th epoch model). (1) mAP = 63.62%; (2) log-average miss rate. (Image source: drawn by the authors). capture its obvious features. However, in the plant and stain categories, regardless of the model at which stage, the LAMR is always high (up to 0.90). This shows that this type of surface attachment or color spot interference feature is still a recognition weakness of the current model.