## 4 Results
### Model Testing
This \"high response + wide coverage\" heatmap feature not only shows that Model 3 can effectively identify the dominant damage area but also has a strong ability to capture microscopic damage, showing that it has stronger adaptability and application potential when facing the complex, changeable, and highly heterogeneous surface weathering phenomenon of the walls of Macau's historical buildings. In the damage quantification test of the training set samples, by comparing images no. 1 to no. 8 in Figure 11 one by one, we can observe the performance differences of each model in terms of the damage-type distinction and brick-level selection accuracy in more detail. In image no. 1, only Model 2 successfully identified the crack label of a brick in the upper right corner, while Models 1 and 3 failed to identify the crack, indicating that Model 2 is more sensitive in extracting local detail features. In image no. 2, Model 3 showed the best overall recognition effect, not only accurately identifying cracks and stains but also accurately framing the damaged area in the lower part of the brick surface. Since the study's guidelines say that damage is more serious than stains, Model 3's ability to focus on identifying damage labels makes its performance more accurate and logical. Image no. 3 further verifies the robustness of Model 3. The obvious surface peeling and weathering marks in the upper half are accurately marked as \"damage\" rather than mistakenly marked as \"stain\". In contrast, Models 1 and 2 are confused about their judgment regarding this area. In image no. 4, both Model 1 and Model 2 misclassified the damaged area as moss, obviously failing to correctly extract the texture features of material erosion. Although Model 3 made more accurate judgments overall, it still misclassified a missing area as damage. This is because the bricks in this area are not entirely absent; instead, the surface has peeled off more, which causes confusion regarding the label boundary. In image no. 5, Model 3 accurately identified most areas but misclassified one \"damage\" area as vandalism. This may be because the surface morphology of the brick is relatively abnormal, and the texture mutation is misjudged by the model as a sign of human intervention. In image no. 6, Model 3 shows a clear advantage and accurately judges the damaged properties of the second row of bricks, while Models 1 and 2 misclassify the area as missing, obviously confusing the bricks with an intact structure but a peeling surface with the empty bricks that are completely peeled off. In addition, Model 3 also performs well in other label recognition in this image, and the overall output result is stable and dear. In image no. 7, Model 3 accurately frames all cracked bricks, while Model 1 and Model 2 have some missed detections and false detections, which shows that Model 3 has a stronger perception and selection consistency on crack samples. Image no. 8 is a more challenging sample. All three models incorrectly label some types of damage caused by nature, like breakage or moss, as vandalism. This shows that the way we label these damages is often confusing and needs to be improved as we gather more data and adjust the model settings. In the new test images, we further verified the generalization performance of the model under non-training set conditions (Figure 12). Overall, we noticed that Model 1, Model 2, and Model 3 all missed some detections and made some false detections, especially with the blue brick surfaces seen from far away, like in image no. 1 to image no. 6. This shows that the model struggles to identify brick damage when viewed from a distance and the details are unclear. However, when we looked at image no. 7 of Figure 12, which was taken up close, the missed detections were much lower, and the accuracy of recognition was much better. However, under the close-up shooting condition of image no. 7 of Figure 12, the missed detection rate is significantly reduced, and the recognition accuracy is significantly improved.