## 4 Performance Evaluation and Validation  
### Evaluation Method  
Figure 4 illustrates the evaluation methodology used to assess the performance of the proposed damage detection model. The overall process is divided into two main stages:  
Figure 3: U-Net-based multi-class damage detection pipeline for UAV imagery. model training and model validation. In the training phase, 80% of the annotated image dataset was used to generate ground-truth (GT) data for supervised learning. The remaining 20% of the images were reserved as validation data to test the model's predictive performance. This split ensures that the model is evaluated on unseen data, providing an objective measure of its generalization capability. To validate the effectiveness of the proposed method, this study employed a confusion matrix, a widely used evaluation tool in deep-learning-based image analysis tasks [9; 10; 16; 21]. A confusion matrix provides a comprehensive summary of the performance of a classification model by quantifying correct and incorrect predictions across different classes. As shown in Table 6, the confusion matrix consists of four key components: true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN), which are used to compute evaluation metrics. The evaluation metrics derived from the confusion matrix in Table 6 are defined by the following equations:  
\[\text{Precision (PR)}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{1}\]  
\[\text{Recall (RE)}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{2}\]  
\[\text{Accuracy (AC)}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}+\text{TN}+ \text{FN}} \tag{3}\]  
\[\text{F1 Score (F1)}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}} \tag{4}\]  
Such evaluation methods require careful consideration when the dataset contains a disproportionately large amount of non-damage (background) data. [PERSON] [29] emphasized that although background regions may be well classified due to their prevalence in \begin{table}
\begin{tabular}{l l l} \hline \hline  & **Predicted Positive** & **Predicted Negative** \\ \hline
**Actual Positive** & True Positive (TP) & False Negative (FN) \\
**Actual Negative** & False Positive (FP) & True Negative (TN) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Definition of confusion matrix. Figure 4: Evaluation process of model training and validation. training data, this can result in artificially high accuracy scores while failing to detect actual damage effectively. Consequently, this can lead to low detection sensitivity and reduced reliability in real-world applications. To overcome the limitations of using accuracy as a sole performance metric, it is essential to incorporate additional evaluation indicators, such as the Precision, Recall, and F1-Score. Precision quantifies the proportion of correctly identified damage among all predicted damage regions. Recall measures the model's ability to detect all actual damage instances. The F1-Score provides a balanced harmonic mean of the Precision and Recall, offering a more comprehensive assessment of the model's effectiveness. In this study, the performance of the proposed method was evaluated using these metrics across five distinct damage classes. As illustrated in Figure 5a, ground-truth annotations were carefully defined for evaluation purposes. Each damage instance was manually labeled based on its type: cracks were annotated using line drawings, while concrete scaling/spalling, exposed rebar, water leakage, and paint peeling were annotated using polygonal regions to accurately delineate their spatial extent. Figure 5b presents sample images with the predicted damage regions overlaid, as identified by the trained model using 80% of the dataset for ground-truth (GT)-based training. These predictions reflect the model's ability to detect damage based on learned features. Figure 5c illustrates the overlap between the predicted damage regions and the corresponding ground-truth annotations.