## 3 Experimental Results and Discussion
### Validation Test of the Proposed Crack Detection Model  
For verifying the robustness of the proposed model, a validation test was conducted in this study. Various methods were employed to obtain new images with cracks such as online searching, experimental collection, and public datasets. Finally, the validation set contains 120 images, and the total number of crack samples is 156, including 38 transverse cracks, 74 longitudinal cracks, 18 block cracks, and 26 alligator cracks. To obtain the experimental results, the validation set was input into the proposed crack detection model. Examples of the experimental results are shown in Figure 10.  
It can be seen from Figure 10 that the bounding boxes generated by our proposed model describe the transverse and longitudinal cracks well as shown in Figure 10a,b, while those for block and alligator cracks are not particularly accurate as shown in Figure 10c,d, which is consistent with the experimental results obtained by the test set. This also shows that the proposed model has a strong ability to detect transverse and longitudinal cracks of a small size, but at the expense of deteriorating the ability to extract the features of block and alligator cracks. Table 5 displays the missed detection rate and the false detection rate of each category of cracks in the validation set. According to statistics, 150 crack samples were detected and 5 crack samples were not detected, including 1 transverse crack, 3 longitudinal cracks, and 1 block crack. For the entire validation set, the missed detection rate is 3.31%, which is slightly higher than that of our test set. In addition, a transverse crack was incorrectly detected as a longitudinal crack due to the different shooting angle, as  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Detection Network**} & \multicolumn{4}{c}{**Missed Detection Rate**} & \multicolumn{2}{c}{**False Detection Rate**} \\ \cline{2-7}  & **Transverse** & **Longitudinal** & **Block** & **Alligator** & **Test Set** & **Test Set** \\ \hline YOLOv5 & 13.33\% & 9.70\% & 3.13\% & 6.41\% & 8.95\% & 0\% \\ The proposed network & 2.96\% & 2.53\% & 0\% & 1.92\% & 2.20\% & 0\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of evaluation indicators in engineering before and after network improvement.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Detection Network**} & \multicolumn{4}{c}{**Average Precision (AP)**} & \multicolumn{2}{c}{**Mean Average Precision (mAP)**} \\ \cline{2-5}  & **Transverse** & **Longitudinal** & **Block** & **Alligator** & **Test Set** \\ \hline Faster-RCNN & 66.27\% & 70.44\% & 92.24\% & 84.87\% & 78.46\% \\ YOLOv3 & 72.62\% & 75.83\% & 98.31\% & 90.13\% & 84.22\% \\ YOLOv5 & 73.54\% & 77.26\% & 98.38\% & 90.42\% & 84.90\% \\ The proposed network & 81.75\% & 83.81\% & 98.20\% & 92.83\% & 89.15\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of experimental results of the selected networks.  
shown in Figure 10e, which leads to a false detection rate of the longitudinal crack of 0.12%. It can be concluded from the above experimental results and analysis that the proposed crack detection model has achieved a good performance on the validation set and is of good adaptability.