## 4 Discussion
### Evaluation Metrics  
Several evaluation factors were used for quantifying the classification tasks [43]. Precision (Equation (1)) determines what percentage of the examples that the model predicted as a particular class actually belong to that class. \[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{1}\]  
where TP (True Positive) is the number of cases that the model correctly identified as that class, FP (False Positive) is the number of cases that the model incorrectly identified as that class, Recall (Equation (2)) determines what percentage of the total number of true examples of a class the model correctly identified and FN (False Negative) is the number of cases that the model incorrectly identified as another class. \[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{2}\]  
F1-Score harmonic the means of Precision and Recall (Equation (3)), so that, when one of these two values is low, the F1-Score also goes down. \[\text{F1-Score}=2\times\frac{\text{Precision}+\text{Recall}}{\text{Precision} \times\text{Recall}} \tag{3}\]  
The Balanced Accuracy (BA) metric is better than simple accuracy when the data are unbalanced, measuring the average of Sensitivity and Specificity (Equation (4))  
\[\text{BA}=\frac{\text{Sensitivity}+\text{Specificity}}{2} \tag{4}\]  
where Sensitivity is defined the same as Recall, while Specificity is the rate of correct identification of negative samples (Equation (5)):  
\[\text{Specificity}=\frac{\text{TN}}{\text{TN}+\text{FP}} \tag{5}\]  
TN (True Negative) is the number of negative classes predicted to be negative classes [43]. Tables 5 and 6 compare the performance of six different models for concrete damage classification using the CODEBRIM dataset. Four metrics are used to evaluate the ResNet (18, 50 and 101); ResNeXt (50 and 101) and EfficientNet-B3 models: Precision, Recall, Balanced Accuracy and F1-Score. These models' architecture and complexity differ, causing them to perform better in some classes and worse in others. Among the ResNet models, ResNet-18 demonstrates the weakest performance, with lower F1-Scores and Balanced Accuracy across all defect classes. This is particularly evident in the \"corrosion stain\" category, where its F1-Score is only 0.761, suggesting difficulties in correctly distinguishing this defect from others. ResNet-50 improves upon ResNet-18 in all the metrics, especially in detecting \"crack\" and \"spalling\", where their F1-Scores reach 0.845 and 0.813, respectively. ResNet-101 further enhances the classification performance, achieving the highest scores within the ResNet family, particularly in identifying \"efflorescence\" and \"exposed bars\", where its F1-Scores surpass 0.89. ResNeXt-50 and ResNeXt-101, which utilize grouped convolutions for better feature extraction, outperform their ResNet counterparts. ResNeXt-50 surpasses ResNet-50 in nearly all the categories, with a notable improvement in \"crack\" (F1-Score of 0.879) and \"spalling\" (F1-Score of 0.824). ResNeXt-101 further refines this performance, providing an overall Balanced Accuracy of 0.935 and particularly excelling in \"efflorescence\" and \"corrosion stain\", where its F1-Scores exceed those of ResNet-101. The improvements in the ResNeXt models suggest that adopting grouped convolutions enhances feature representation and helps in better distinguishing similar structural defects. EfficientNet-B3 emerges as the best-performing model among all the tested architectures. It demonstrates superior Recall in detecting \"crack\" (0.947), meaning it successfully identifies nearly all instances of this defect. Moreover, in the \"corrosion stain\" category, EfficientNet-B3 achieves the highest F1-Score of 0.878, highlighting its effectiveness in handling difficult-to-distinguish defects. In the \"background\" class, this model records the highest Recall (1.000) and F1-Score (0.943), showcasing its Precision in correctly classifying defect-free areas.