## 4 Results
### Model Testing  
According to the results of the thermal map, the performances of Model 1, Model 2, and Model 3 in the actual image test can be confirmed. Model 3 shows a stronger, denser, and more focused response on the real gray brick damage area in most cases, and its overall performance is better than Model 1 and Model 2, especially in images with complex visual features and uneven damage distribution (Figure 9). From typical samples such as image numbers 1, 4, 5, 6, and 8, Model 3 shows a larger area and higher response intensity (the red area is denser) in the thermal map, accurately covering various types of gray brick damage such as cracks, breakage, peeling, weathering, and plant erosion. For example, in image 1 of Figure 9, Model 3 clearly captures the continuity of the brick joints and the fractured areas of the brick surface, while the responses of Model 1 and Model 2 are weak and discontinuous. In image no. 6 of Figure 9, multiple depressions and holes on the brick surface also obtain concentrated and consistent high responses in the thermal map of Model 3. However, it should be pointed out that the high-response areas marked by the three groups of models do not always accurately correspond to the specific locations of actual damage. This is mainly because the facade of a single brick was used as the frame selection unit during the marking stage in this study rather than accurate marking based on damage details. This strategy has affected the complete correspondence between the thermal map and the damaged location to a certain extent. After using heatmap visualization to test the new dataset (Figure 10), we found that the three models performed well on images they had not seen before, showing they can recognize damage consistently. Compared with the previous test based on the training set, this round of experiments used gray brick image samples that the model had never seen, including various brick-type arrangements, weathering degrees, and differences in gray joint treatment. This setting is closer to the actual application scenario and can fully verify the performance of the model in a real environment. Overall, Model 3 still shows the best recognition response and regional focusing capabilities and has stronger cross-image generalization capabilities and damage perception robustness. Specifically, in image no. 3 of Figure 10, Model 3 accurately responds to the area with more serious cracks on the brick surface, and the heatmap of Model 3 forms a concentrated and continuous red high-response area there. Models 1 and 2 show slight response diffusion or blurred boundaries, indicating that their ability to locate damage is relatively weak when facing unlabeled images. Additionally, image no. 5 of Figure 10, despite showing relatively light damage overall and having mostly intact bricks, still contains numerous details such as slight weathering, pollution spots, and microcracks. Model 3 shows a stronger ability to capture details. The response hotspots are not only large in number and evenly distributed but also show excellent damage sensitivity. It is particularly noteworthy that in images no. 5 to no. 7 of Figure 10 with more background interference (such as light-colored brick joints, uneven lighting, etc. ), the response boundaries of the three models are blurred, and the noise response increases (blue at the edge of the image), and Model 2 has a lack of effective activation in local areas (image no. 2 of Figure 10), indicating that it still has certain limitations in feature extraction and structural cognition. In summary, Model 3 has a relatively stronger generalization ability for different types of damage. Its heatmap shows a higher overall activity in most images, and the red area is richer. This shows that it is more sensitive to gray brick damage and has better recognition density and response stability, reflecting that the model has successfully developed a more mature mechanism for extracting damage features in the later stage of training.