## 4 Model Evaluation
### Backbone Network  
The backbone network is responsible for extracting features that will be processed and classified along the convolution layers of the algorithm. A higher number of convolution layers represents a greater classification capacity; however, it may lead to premature overfitting and consequently less favorable metrics. Deeper networks also tend to overload computational resources in such a way that make their use unfeasible. Table 7 shows the values of the main metrics obtained for the Resnet-50 as well as Resnet-101, both including FPN (see Section 3.2) and considering the best hyperparameters identified in the previous sections. The results show the superiority of the Resnet-101 network in all the precision metrics, especially for AP\({}_{50}\)\({}^{\text{Detect}}\) and AP\({}_{50}\)\({}^{\text{Segm}}\) that increased more than 10%. The Resnet-50 network presents better results for larger instances. In turn, Resnet-101 detects a greater amount of small and medium instances and in large instances is less efficient. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**RoI IoU 0.55**} & \multicolumn{2}{c}{**RoI IoU 0.57**} & \multicolumn{2}{c}{**RoI IoU 0.60**} \\ \cline{2-7}  & \multicolumn{1}{c}{**Maximum Value (\%)**} & \multicolumn{1}{c}{**Iteration**} & \multicolumn{1}{c}{**Maximum Value (\%)**} & \multicolumn{1}{c}{**Iteration**} & \multicolumn{1}{c}{**Value (\%)**} & \multicolumn{1}{c}{**Iteration**} \\ \hline AP\({}_{50}\)\({}^{\text{Detect}}\) & 56.4 & 24,499 & 57.1 & 35,999 & 25.5 & 16,999 \\ \hline AP\({}_{50}\)\({}^{\text{Segm}}\) & 51.8 & 26,499 & 51.9 & 35,999 & 21.1 & 9999 \\ \hline AP\({}_{\text{L}}\)\({}^{\text{Detect}}\) & 46.2 & 24,999 & 46.3 & 38,999 & 26.0 & 18,499 \\ \hline AP\({}_{\text{L}}\)\({}^{\text{Segm}}\) & 33.3 & 24,999 & 33.4 & 29,499 & 15.7 & 16,999 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Metrics values for models RoI IoU 0.55, 0.57, and 0.60. Figure 14 shows the training and validation curves for the loss function for the Resnet-50 and Resnet-101 networks. It is possible to notice an enhanced tendency of overfitting for Resnet-101 in comparison to Resnet-50, as stated by the greater end values of the validation loss function (0.31 against 0.24). This behavior was expected due to the increased number of parameters handled by the Resnet-101 network. Figure 15 shows three examples of classification (EX1 to EX3) performed by both networks based on images from the inference dataset. It is possible to notice the improvement provided by Resnet-101 network, particularly in the classification of minor corroso, as stated by Example 1, although not all instances were identified as revealed by the comparison with the ground truth. Example 2 shows that Resnet-50 and Resnet-101 detected several corrosotions not identified in the ground truth, demonstrating some extrapolation capacity of this type of algorithms. In this situation, Resnet-101 performed a better classification by not interpreting a dark spot as an anomaly. This capacity comes from the higher number of convolutional channels of this network, which allows the information to be acquired in a more granular level as the depth is increased [34]. Example 3 shows a case in which the poor lighting imposed limitations in the identification of small corrosotions on the image labelling, but when viewing the image in detail, particularly in the extremities of the panels, it is possible to confirm that the pathology exists, and therefore, the algorithm again