## 5 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Training Time** & **Inference Time** & **Precision** & **Recall** & **F1** & **mAP (COCO)** & **mAP (Pascal VOC)** & **MACs** \\ \hline Faster R-CNN & 324 s & 77.4 ms & 57.86\% & 89.90\% & 60.35\% & 34.14\% & 75.46\% & 18.410 G \\ MobileNetV3-Large-7 FN & 214 s & 60.1 ms & 42.76\% & 39.81\% & 41.23\% & 8.27\% & 30.97\% & 2.807 G \\ MobileNetV3-Large-320-FPN & 90 s & 19.0 ms & 80.29\% & 91.30\% & 85.44\% & 54.03\% & 87.87\% & 24.438 G \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The results of the models in Group 2. Figure 8: PR curves and confusion matrices of models in Group 2: (**a**) PR curve of Faster R-CNN MobileNetV3-Large-FPN; (**b**) confusion matrix of Faster R-CNN MobileNetV3-Large-FPN; (**c**) PR curve of Faster R-CNN MobileNetV3-Large-320-FPN; (**d**) confusion matrix of Faster R-CNN MobileNetV3-Large-320-FPN; (**e**) PR curve of YOLOv5m; (**f**) confusion matrix of YOLOv5m. The networks in the third group are all single-stage models, and their results are shown in Table 5. The first three models are similar in that their recall is high and their precision is relatively low. As shown in their confusion matrix, all three models yielded more FPs relative to the models in the first two groups. The results of SSD300 VGG16 are more balanced, and the model has the lowest computational complexity. The PR curves and confusion matrices are shown in Figure 9.  
In Group 4, the MACs values of the Faster-R-CNN-based models were much larger than those of YOLOv5l, and the mAP (Pascal VOC) values were very close, but the mAP (COCO) was slightly lower compared with YOLOv5l. The imbalance between precision and recall was also the main drawback of the first two models, as mentioned for Group 3. The results are shown in Table 6, and the PR curves and confusion matrices are shown in Figure 10.  
Overall, all the models were more accurate in detecting sealed cracks than cracks. We believe that sealed cracks are more clearly characterized than cracks. It also can be seen that the low-resolution models performed worse compared with the high-resolution models. It should be noted that all the PR curves have been truncated because detected instances with confidence scores less than 0.25 and IoUs less than 0.5 have been filtered. All the trained models are publicly available. Images that never appeared in the training and validation sets were used for testing, in order to understand the capabilities of the different models more intuitively. Some representative results are shown in Figure 11. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{\begin{tabular}{c} **Training** \\ **Time** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Inference** \\ **Time** \\ \end{tabular} } & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**F1**} & \multirow{2}{*}{\
\begin{tabular}{c} **mAP** \\ **(COCO)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **mAP** \\ **(Pascal** \\ \end{tabular} } & \multirow{2}{*}{**MACs**} \\ \hline Faster R-CNN ResNet50-FPN & 657 s & 97.3 ms & 57.54\% & 95.52\% & 71.82\% & 42.29\% & 84.61\% & 133.929 G \\ Faster R-CNN ResNet50-FPN v2 & 848 s & 95.3 ms & 59.57\% & 94.88\% & 73.19\% & 42.95\% & 84.91\% & 280.797 G \\ YOLOv51 & 146 s & 25.3 ms & 80.87\% & 91.23\% & 85.74\% & 53.57\% & 87.65\% & 32.700 G \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The results of the models in Group 4. Figure 9: PR curves and confusion matrices of models in Group 3: (**a**) PR curve of FCOS ResNet50-FPN; (**b**) confusion matrix of FCOS ResNet50-FPN; (**c**) PR curve of RetinaNet ResNet50-FPN; (**d**) confusion matrix of RetinaNet ResNet50-FPN; (**e**) PR curve of RetinaNet ResNet50-FPN v2; (**f**) confusion matrix of RetinaNet ResNet50-FPN v2; (**g**) PR curve of SSD300 VGG16; (**h**) confusion matrix of SSD300 VGG16.