## 4 Results
### Model Training Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Epoch** & **Class** & \
\begin{tabular}{c} **Average** \\ **Precision** \\ \end{tabular} &
\begin{tabular}{c} **Log-Average** \\ **Miss Rate** \\ \end{tabular} & \multicolumn{1}{c}{**F1 ***} & \multicolumn{1}{c}{**Precision ***} & \multicolumn{1}{c}{**Recall ***} \\ \hline \multirow{6}{*}{64} & Crack & 0.67 & 0.55 & 0.62 & 0.66 & 0.57 \\  & Damage & 0.71 & 0.65 & 0.68 & 0.7 & 0.66 \\  & Intact & 0.41 & 0.72 & 0.43 & 0.6 & 0.33 \\  & Missing & 0.72 & 0.5 & 0.67 & 0.57 & 0.78 \\  & Moss & 0.76 & 0.51 & 0.7 & 0.59 & 0.84 \\  & Plant & 0.49 & 0.9 & 0.42 & 0.45 & 0.38 \\  & Stain & 0.37 & 0.88 & 0.45 & 0.43 & 0.46 \\  & Vandalism & 0.95 & 0 & 0.75 & 0.75 & 0.75 \\ \hline \multirow{6}{*}{90} & Crack & 0.74 & 0.45 & 0.58 & 0.52 & 0.64 \\  & Damage & 0.7 & 0.58 & 0.69 & 0.67 & 0.69 \\  & Intact & 0.23 & 0.87 & 0.34 & 0.35 & 0.33 \\  & Missing & 0.69 & 0.56 & 0.65 & 0.55 & 0.78 \\  & Moss & 0.7 & 0.57 & 0.68 & 0.6 & 0.78 \\  & Plant & 0.55 & 0.83 & 0.62 & 0.61 & 0.61 \\  & Stain & 0.4 & 0.83 & 0.51 & 0.5 & 0.51 \\  & Vandalism & 1 & 0 & 1 & 1 & 1 \\ \hline \multirow{6}{*}{297} & Crack & 0.71 & 0.42 & 0.69 & 0.66 & 0.71 \\  & Damage & 0.64 & 0.7 & 0.67 & 0.65 & 0.69 \\  & Intact & 0.27 & 0.76 & 0.33 & 0.41 & 0.27 \\  & Missing & 0.67 & 0.6 & 0.73 & 0.63 & 0.85 \\  & Moss & 0.74 & 0.51 & 0.74 & 0.62 & 0.89 \\  & Plant & 0.46 & 0.83 & 0.55 & 0.5 & 0.61 \\  & Stain & 0.42 & 0.82 & 0.52 & 0.51 & 0.51 \\  & Vandalism & 1 & 0 & 1 & 1 & 1 \\ \hline \hline \end{tabular}  
* indicates score threshold = 0.5; recall
* indicates score threshold = 0.5; precision
* indicates score threshold = 0.5.  
\end{table} 
Table 4: Model performance statistics for different epochs. non-damaged textures, and the problems of misjudgment and missed judgment have not been eradicated. Looking more closely at how well the three-stage model identifies different types of gray brick damage, using the confusion matrix helps us to better understand how well the YOLOv8 model can tell the difference between various labels and where it tends to make mistakes in real situations (Figures 6-8). Model 1 had a high accuracy rate of 0.79 for identifying missing bricks, showing it can reliably spot missing damage; it scored 0.57 for cracks, 0.68 for damage, and 0.89 for moss. Model 1 achieved a high recognition accuracy rate (0.79) in the missing category, showing a stable ability to distinguish brick-missing damage, while crack, damage, and moss reached 0.57, 0.68, and 0.89, respectively. In particular, the recognition of the moss category was the most stable, with almost no confusion. In the plant, stain, and intact categories, the accuracy rate was much lower, especially for stain and intact, which were only 0.52 and 0.44, respectively, and many were wrongly identified as damage, background, or each other. This shows that the model struggles to see the small differences between surface stains, natural attachments, and the brick surface itself. It is important to note that the vandalism category does well in all models (with an accuracy close to 1.00) because it has clear texture edges, noticeable local changes, fewer differences in training samples, and is easier for the model to learn. It should be pointed out that although the vandalism category performs well in all models (with an accuracy close to 1.00), this improvement is mainly due to its clear texture edges, obvious local structural mutations, small differences in training samples, and ease of being efficiently learned by the model. Figure 6: Confusion matrix of Model 1 (64 th epoch model). (Image source: drawn by the authors). Figure 8: Confusion matrix of Model 3 (297 th epoch model). (Image source: drawn by the authors). Figure 7: Confusion matrix of Model 2 (90 th epoch model). (Image source: drawn by the authors).