## 3 Results and Discussion
### Raw Annotated Data (RD)  
The summary of the results for the five sets of bridges for RD is presented and discussed. The models developed by training the RD dataset for the FRNB showed that the precision values for the balanced models ABM and BM were 0.631 and 0.667, while that of the CM, AM1, and AM2 models were 0.269, 0.264, and 0.282, respectively. The precision increased by an average of 80% when the pixels were balanced (Figure 5). The least performing metrics are seen in the unbalanced models (CM, AM1, and AM2) except for GA and mean IOU. The BM model was seen to have better performance metrics than the ABM model, except for GA metrics. The F1 score for ABM and BM (balanced) models is 0.416 and 0.427, while that of the augmented models (AM1 and AM2) is 0.284 and 0.294, respectively. The F1 score of the CM is 0.295. The F1 score increased by an average of 47% after balancing the pixels, while it remained almost the same for the augmented models. The average GA for AM1 and AM2 is 0.720, while that of the BM and ABM is 0.623. Balancing the dataset caused a drop in the GA by 15%. Contrarily, by computing the mean accuracies (MA), the average values of the ABM and BM models increased by 14% compared to the AM1 and AM2 models. The TNR for the balanced models is the highest, with values of 0.872 and 0.863, respectively, compared to the base and augmented models, which have values of 0.813, 0.810, and 0.811, respectively. There was no significant change in the TNR values for the CM and the augmented models. Considering the RD dataset for FRSB, the CM model showed the least performance considering precision, TNR, and F1 score (Figure 6). Figure 5: Evaluation metrics for FRNB for RD. In contrast, AM1 and AM2 models showed the least performance for MA, TPR, and TNR metrics except for GA, precision, mean IOU, and F1-score. The results depicted that the balanced models, BM and ABM, developed from the RD increased the model's performance. For instance, the MA, precision, TPR, TNR, and F1-score for the balanced models increased by an average of 16%, 175%, 5%, 8%, and 53%, respectively, when compared to the least performing augmented model. CM had a higher GA value of 0.736 and outperformed the balanced dataset by 20% (Figure 6). This is contrary to when MA is considered. The MA of the balanced models outperformed the CM, AM1, and AM2 models by 19%. This shows that MA may be a preferred evaluation metric over GA. The GA does not account for the effect of the biased nature of the unbalanced dataset. The preference of MA over precision for a balanced dataset is due to the balanced models outperforming other models for most evaluation metrics. The scale, shear, and rotation augmentation parameters for AM2 had to significant effect on the performance of the models. The BM and ABM models developed from the RD for the PRMD bridge showed a significant increase in the MA, precision, TPR, TNR, and F1 scores. In comparison to the least performing augmented model, results showed that MA, precision, TPR, TNR, and F1-score increased by an average of 17%, 143%, 11%, 8%, and 52%, respectively. Conversely, the AM1 models performed the least for all metrics except GA and mean IOU. The models developed by training the RD dataset for the PRNB bridge show that the MA, precision, TPR, TNR, and F1-score of the balanced models increased by an average of 3.6%, 135%, 1%, 3%, and 37% compared to the least performing augmented (AM1) model (Figure 7). In addition, the precision increased from 0.281 for the CM model to 0.674 and 0.261 for the AM1 model to 0.362 for the BM models. The maximum GA, MA, precision, TPR, TNR, and FI scores are 0.644 and 0.526, 0.674, 0.257, 0.793, and 0.362, respectively. The models developed by training the RD dataset for the PRSB bridge show that the MA, precision, TPR, TNR, and F1-score of the BM model increased by 17%, 150%, 3.5%, 5.5%, and 50%, respectively, when compared to the least performing augmented model.