# Crack Segmentation on Earthen Heritage Site Surfaces
## 4 Results and Discussion  
All models were trained for 100 epochs with a batch size of 4 for all models. Table 4 shows the metrics presented for the validation set.  
Metrics of U-Net, As shown in Figure 4, Precision achieved the highest score at epoch 36, but the Recall score was only 40.38%, which means more cracked pixels were classified as intact. After completing training based on 100 epochs, the Precision score was 88.08%, but the Recall value was only 74.59%. Metrics of Linknet and FPN were also analyzed, shown in Figures 5 and 6, revealing similar results. Furthermore, the authors checked the predicted outputs, as shown in Figure 7, the crack annotation was incomplete for the predicted result and there was noise in the background. In other words, neither the Precision nor Recall score achieved good results. Moreover, the IoU value was 67.43%, which also shows that the prediction result did not match the ground truth very well. Among the results predicted by U-Net-densenet201, U-Net-resnet152, FPN-resnet152, and FPN-inceptionv3 models, some cracked pixels were classified as intact, which indicates failure in detecting some cracked pixels. In the results predicted by the Linknet-densenet201, Linknet-inceptionv3, FPN-densenet201 models, there was too much noise in the background.  
U-Net-vgg16, Linknet-vgg16, and FPN-vgg16 all performed well without noise in the background. As can be seen in Figures 4-6, the results of completing training based on 100 epochs had very close F1 scores. According to Table 4, FPN-vgg16 obtained the highest F1 score of 84.40% and the highest IoU score of 73.11%, which indicates the highest level of applicability in crack detection of earnthen heritage sites among all networks in this study. FPN-vgg16 demonstrated unexpected performance and has fewer training parameters than other networks when F1 and IoU scores are compared and the prediction result image is analyzed.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Backbones** & **Precision\%** & **Recall\%** & **F1 Score\%** & **IoU\%** & **Best Epoch \({}^{*}\)** \\ \hline \multirow{4}{*}{Unet} & vgg16 & 89.41 & 85.52 & 83.65 & 72.00 & 84 \\  & resnet152 & 93.02 & 79.85 & 82.28 & 69.98 & 93 \\  & densenet201 & 92.60 & 94.38 & 83.68 & 72.01 & 80 \\  & inceptionv3 & 95.21 & 80.66 & 82.65 & 70.51 & 84 \\ \hline \multirow{4}{*}{Linknet} & vgg16 & 90.19 & 85.97 & 82.76 & 70.74 & 99 \\  & resnet152 & 90.40 & 83.07 & 82.63 & 70.43 & 76 \\  & densenet201 & 89.80 & 81.56 & 81.83 & 69.33 & 96 \\  & inceptionv3 & 88.03 & 80.20 & 80.88 & 67.99 & 88 \\ \hline \multirow{4}{*}{FPN} & vgg16 & 88.83 & 85.82 & 84.40 & 73.11 & 93 \\  & resnet152 & 90.50 & 83.35 & 83.31 & 71.46 & 89 \\ \cline{1-1}  & densenet201 & 87.84 & 82.62 & 82.59 & 70.44 & 90 \\ \cline{1-1}  & inceptionv3 & 90.05 & 85.37 & 81.72 & 69.25 & 94 \\ \hline \hline \end{tabular}  
* Epoch where the highest F1 score was obtained for the validation set.  
\end{table}
Table 4: The metrics are presented for the validation set.  
Figure 4: The metrics Precision, Recall, IoU, and F1 score as obtained from U-Net for different backbones: (**a**) vgg16, (**b**) resnet152, (**c**) densenet201, (**d**) inceptionv3.  
Figure 5: The metrics Precision, Recall, IoU, and F1 score as obtained from Linknet for different backbones: (**a**) vgg16, (**b**) resnet152, (**c**) densenet201, (**d**) inceptionv3.  
Figure 6: The metrics Precision, Recall, IoU, and F1 score as obtained from FPN for different backbones: (**a**) vgg16, (**b**) resnet152, (**c**) densenet201, (**d**) inceptionv3.  
Figure 7: _Cont._