## 4 Results
### Model Training Results
We speculate that due to the high degree of confusion between its texture and background or the subjective differences in the annotation boundaries, further optimization is still needed in terms of data balance and fine-grained annotation strategies. As shown in Figure 5 and Table 4, the 64 th epoch model achieved relatively balanced results in multiple key indicators, especially in the vandalism category. All models showed stable and extremely high recognition capabilities (AP = 0.95 or 1.00, LAMR = 0.00, F1 = 0.75 or 1.00). This evidence shows that the characteristics of this type of damage are obvious, and the model can accurately capture them. However, in the remaining categories, the performance of the 64 th epoch model is more robust, especially in the detection of highly complex surface damage, such as missing (AP = 0.72, recall = 0.78) and moss (AP = 0.76, recall = 0.84). This result shows that the model at this stage can effectively learn target features with strong texture and fuzzy boundaries. At the same time, the F1 value of structural damage, such as damage and cracks, is also close to or exceeds 0.6, proving the stability of the model in extracting spatial continuity features. Although the performance of plant, stain, and intact classes is still relatively low, especially the recall and F1 values are obviously insufficient (e.g., recall of plant = 0.38), these interfering features have strong visual uncertainty in the original image, which is a common difficulty in current building damage detection. Figure 5: Model performance statistics at different training epochs. (Asterisk * in the figure indicates the median.) In the figure, F1 * indicates score threshold = 0.5; recall * indicates score threshold = 0.5; precision * indicates score threshold = 0.5. (Image source: drawn by the authors). In comparison, even though the overall mAP of the 90 th epoch model went down slightly to 62.60%, the model did well at recognizing cracks (AP = 0.74), showing it is better at noticing fine details. However, the model's precision and recall for the intact category are very low (precision = 0.35, recall = 0.33), and the F1 value is just 0.34. However, the precision and recall of the model in the intact category are extremely low (precision = 0.35, recall = 0.33), and the F1 value is only 0.34. This indicates that the model's ability to discriminate undamaged areas has declined, which may be due to the excessive reinforcement of the learning of the damage category in the later stage of training, resulting in insufficient generalization ability of the non-damaged category. In addition, although the F1 values of the plant and stain categories have increased slightly, the LAMR remains high (both 0.83), indicating that the problem of missed detection has not been fundamentally improved. At the 297 th epoch model stage, although the overall mAP further dropped to 61.51%, some categories showed more mature detection capabilities, especially in the moss and missing categories, with recall reaching 0.89 and 0.85, respectively, and F1 reaching 0.74 and 0.73. This result shows that the model's detection coverage of common damaged targets has been significantly improved at the end of training, and the missed detection rate has been controlled (LAMR = 0.51 and 0.60). At the same time, the recall of the crack category rose to 0.71, and the precision also reached 0.66, indicating that the model, at this stage, has achieved a coordinated improvement in detection accuracy and recall rate for crack-type targets. However, like the previous stage, the performance of the three categories of intact, plant, and stain is still lagging behind, especially the recall of intact is only 0.27, indicating that the model still finds it difficult to extract effective features from