## 3 Results and Discussion
### Background Annotated Data (BD)  
The models developed by training the BD dataset for the FRNB showed that the average TNR value of 0.693 for the balanced models is lower than the average value of 0.842 for the base and augmented models. The F1-score increased by 36% from 0.291 for the augmented models to an average of 0.397 for the balanced models. There was no significant change in the precision and MA before and after data balancing, whereas the GA dropped by 8% for the augmented models compared to the balanced models from 0.693 to 0.636 (Figure 9). Similarly, the least performing metrics were seen in the augmented models except for GA and mean IOU, while the balanced models showed higher performance except for the GA, precision, mean IOU, and TNR. The TPR for the balanced models is 110% higher than the unbalanced models, which have TPR values of 0.266 and 0.268, respectively. The balanced models, BM and ABM, developed from the BD for the FRSB dataset outperformed the base and augmented models; CM, AM1, and AM2 when considering performance metrics; and MA, precision, TPR, and F1-score. These metrics increased on  
Figure 8: Confusion matrix for raw annotated dataset for FRNB for (**a**) Base, (**b**)Augmented, (**c**) Balanced, and (**d**) Augmented–Balanced models. Figure 7: Evaluation metrics for PRNB for RD. average by 2.5%, 3.4%, 100%, and 38% compared to the least-performing augmented model (Figure 10). Conversely, the base and augmented models show lower performance for all metrics except for GA and mean IOU. This is a similar trend and pattern for all bridges. This further corroborates the preference of MA for performance evaluation instead of GA. Furthermore, the precision for both CM and balanced models showed the same value of 0.304, while that of the augmented models was 0.294. This implies that balancing the dataset did not affect the precision of the model for the FRSB. Evaluating the performance metrics of the models developed from the BD dataset for PRMD, the results show that the TPR was almost doubled from 0.265 for the least performing augmented model (AM1) to 0.515 for the BM model. Balancing the dataset significantly improved the performance of the models. Similarly, the F1 score increased by an average of 40% after balancing the dataset. The TNR increased by 2% for the BM model compared to the ABM model. The GA for the CM, AM1, and AM2 increased by 12%, 15%, and 8% compared to the least-performing balanced ABM model. The results of the models developed by training the BD dataset for the PRNB bridge show that the TPR, TNR, and F1-score of the balanced models increased by an average of 70% and 23%, respectively, compared to the least performing augmented (AM2) model (Figure 11). Figure 10: Evaluation metrics for FRSB for BD. Figure 9: Evaluation metrics for FRNB for BD. In addition, the TPR increased from 0.297 for the AM2 to 0.504 for the BM model, while the F1-score increased from 0.272 for AM2 to 0.336 for the ABM model. The maximum TPR, TNR, and F1 scores are 0.504, 0.724, and 0.336, respectively. The models developed by training the BD dataset for the PRSB bridge show that the MA, precision, TPR, and F1-score for the BM model increased by 2.5%, 4%, 87%, and 34%, respectively, when compared to the least performing augmented model. The results showed that MA increased from 0.604 for AM2 to 0.622 for the BM model, TPR increased from 0.269 for CM to 0.499 for the BM model, and F1-score increased from 0.280 for CM to 0.374 for the ABM model. Considering GA metrics, AM1 shows the best performance value of 0.680. The confusion matrix for the FRNB background annotated dataset is depicted in Figure 12. Similarly, augmentation of the data caused no significant change in the values of the metrics shown in the confusion matrix. The figure depicts the same trend as the previous; while the TP increased by 6%, the TN reduced by 12%. Consequently, while the FN reduced by 10%, the FP increased by 12%.