## 3 Results and Discussions
### Performance Comparisons of Faster R-CNN and Mask R-CNN  
In the experiments, six network structures (Table 2) were transferred as the backbone structures of the Faster R-CNN and Mask R-CNN and formed 12 models with the corresponding weights being pre-trained on the COCO dataset, and then the models were fine-tuned. The pavement dataset was input into these 12 models to get the corresponding results eventually. (C4: used a ResNet conv4 backbone with a conv5 head [40]. DC5: used a ResNet conv5 backbone with dilations in conv5 and standard conv and FC heads [56]). \begin{table}
\begin{tabular}{c c  
When the LR was 0.001, the classification accuracy of each model after 24,000 iterations was shown in Table 3. The result showed that the classification accuracy of the six Faster R-CNN models was very close, with a minimum of 98.7% under F-R101-FPN and 100% under others; the lowest classification accuracy of the six Mask R-CNN models was M-R50-FPN and M-R101-FPN, with an accuracy of 99.3%, and the others were 100%. It could be seen that the accuracy of the models containing the FPN structure was lower than other models, and the accuracy of the Faster R-CNN and Mask R-CNN algorithms was almost the same. The models were validated 1000 times in 24,000 iterations, and 1000 validation accuracy values were generated. Figure 6a showed the validation accuracy curves of F-R50-FN and M-R50-FPN, which tended to be flat after 400 validations and eventually converged; Figure 6b showed the validation accuracy curves for F-R101-FPN and M-R101-FPN, and both of them converged at the end of the iteration. The validation accuracy curves of the other eight models are shown in the appendix, and the validation curves of the models converged. The training loss curves of F-R50-FPN and M-R50-FPN are shown in Figure 7a, which declined rapidly in 0 to 200 validations, tended to be flat in the range of 200 to 600 validations, and eventually converged. Since the Mask R-CNN had one \(L_{mask}\) more than the Faster R-CNN, the final loss of M-R101-FPN in Figure 7b was slightly higher than F-R101-FPN. However, the final loss values shown in Figure 7a were close, which also validated the superiority of the Mask R-CNN to the Faster R-CNN model in pavement defect detection. The training loss curves A2 and A4 of the other eight models are shown in the appendix, and the loss curves of the models converged at the end of the iteration. The AP was an important indicator of evaluating the model; meanwhile, the testing speed of the model should be ensured with a satisfactory AP. Therefore, this paper compared the AP of the Faster R-CNN and Mask R-CNN with the average testing time of each image. Figure 8a shows the comparison of the AP between the Faster R-CNN and Mask R-CNN models under different backbone structures. The AP of all the Mask R-CNN models was higher than the Faster R-CNN models, and the largest difference in the AP was 2.9% when the backbone structure was R50-DC5. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Backbone Structures**} \\ \cline{2-7}  & \multicolumn{2}{c}{**ResNet-50**} & \multicolumn{3}{c}{**ResNet-101**} \\ \cline{2-7}  & **FPN** & **C4** & **DC5** & **FPN** & **C4** & **DC5** \\ \hline Faster R-CNN & 100\% & 100\% & 100\% & 98.7\% & 100\% & 100\% \\ Mask R-CNN & 99.4\% & 100\% & 100\% & 99.3\% & 100\% & 100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The detection accuracy with different backbone structures. Figure 6: Classification accuracy curves of the training process: (**a**) F-R50-FPN and M-R50-FPN, (**b**) F-R101-FPN and M-R101-FPN. The testing time of the Faster R-CNN and Mask R-CNN combined with different backbone structures are shown in Figure 8b; when the backbone structures contained FPN, the testing time was lower than the models that contained C4 or DC5.