## 3 Results and Discussion
### Manually Cleaned Annotated Data (MD)  
BM and ABM models developed from MD for FRNB show the least performance for all metrics except for precision and F1-score (Figure 13). The models developed from the MD had the lowest performance in comparison to the models developed from the RD and BD. For instance, the average values of the GA, MA, precision, TPR, TNR, and mean IOU for the BD reduced by 20%, 23%, and 16%, 11%, 19%, 34%, and 3%, respectively, when compared to the average values for best-performing models developed from the RD or BD dataset. Considering the MD dataset for FRSB, a similar trend was observed (Figure 14). The augmented models showed lower performance considering all metrics except for GA and mean IOU. The ABM model showed the least GA and the mean IOU values. The effect of balancing was minimal when considering the MA metrics showing an increase of 1%. The effect of balancing was most prominent in the precision and F1 score metrics. The precision and F1 scores increased by 79% and 29%, respectively, compared to the least-performing augmented models. The models developed from the MD dataset for the FRSB showed the lowest average performance compared to the RD and BD models. Evaluating the performance metrics of the models developed from the MD dataset for the PRMD, results show that the MA, precision, TPR, TNR, and F1-score of the BM and ABM models increased by an average of 3%, 90%, 4%, 2%, and 34% compared to the least  
Figure 14: Evaluation metrics for FRSB for manually cleaned annotated data. Figure 13: Evaluation metrics for FRNB for manually cleaned annotated data. performing augmented (AM1) model. In addition, the precision increased from 0.27 for the CM model to 0.511 for the balanced models. The maximum GA, MA, precision, TPR, TNR, and FI scores are 0.597 and 0.513, 0.511, 0.317, 0.705, and 0.391, respectively. Results for the MD-trained models for PRNB showed that the MA, TPR, TNR, and F1-score of the BM and ABM models increased by an average of 2%, 1.5%, 1%, and 25%, respectively, when compared to the least performing augmented model (Figure 15). However, the augmented models had better performance metrics than the balanced models for GA and TPR. The models developed by training the MD dataset for the PRSB bridge showed that the MA, precision, TPR, and F1-score for the BM model increased average by 1.0%, 91%, 2%, and 34%, respectively, when compared to the least performing augmented model. The F1-score for the BD datasets had the same rate of increment. The results showed that precision increased from 0.285 for CM to 0.552 for the ABM model, and the F1-score increased from 0.299 for CM to 0.397 for the BM model. Considering GA metrics, the AM1 model showed the best performance value of 0.680. Similarly, the confusion matrix shown in Figure 16 further depicts the significant effect of balancing the dataset, unlike augmentation, which had no significant contribution. The TP and TN were reduced when compared to the performance of the models developed from the MD and RD. This may be attributed to a reduction in the data size after removing the images with artifacts background from the dataset. Figure 16: Confusion matrix for manually cleaned annotated dataset for FRNB for (**a**) Base, (**b**) Augmented, (**c**) Balanced, and (**d**) Augmented–Balanced models. Figure 15: Evaluation metrics for PRNB for manually cleaned annotated data. The models developed from the manually annotated dataset (MD) performed less. This may have been caused by significantly reducing the datasets by manually removing the images with artifacts and background noises. The summary of the performance metrics showing the minimum and maximum values for the developed models is shown in Table 6. The RD models for all the bridges show the highest GA values ranging from 0.644 to 0.773, while the least-performing model was the PN/R/B with a value of 0.447. Contrarily, for the MA metrics, the balanced models showed the best performance ranging from 0.603 to 0.657, and the least performance value of 0.494 for the ABM model.