## 5 Experiments and Results
### Experimental Results
#### 5.4.2 Comparison Results of Detection Accuracy  
The results of comparing the overall detection accuracy are presented in Table 4. Among all models, the Faster-RCNN model demonstrated the highest accuracy, surpassing the YOLO series models in all evaluation indexes of accuracy. It achieved a precision (**P**), recall (**R**), F1 value, and mean average precision (**mAP**) of 75.6%, 76.4%, 75.3%, and 79.3%, respectively. Among the YOLO series models, YOLOv7-tiny exhibited a lower overall precision with values of 66.9% (**P**), 66.5% (**R**), 66.7% (**F1-score**), and 65.5% (**mAP**). On the other hand, both YOLOv5s and YOLOv8s showed similar overall precision, but were slightly inferior to Faster-RCNN by approximately a margin from around 3% to 5%. The Results of Detection Accuracy under Different Crack Types  
To further clarify the discrepancies in the model recognition accuracy among the different crack categories, a comparative analysis of the model recognition accuracy was conducted for the four types of cracks: longitudinal cracks (**LC**), transverse cracks (**TC**), oblique cracks (**OC**), and mesh cracks (**AC**). The results are presented in Table 5.  
Regarding the identification of longitudinal cracks (**LC**), the Faster-RCNN model exhibited the highest accuracy, with an average precision (AP) of 85.7% and the highest F1 value of 82.3%. In contrast, the YOLO series demonstrated a relatively inferior \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & _Precision_ & _Recall_ & _F1-Score_ & _mAP_ \\ \hline Faster-RCNN & 75.6 & 76.4 & 75.3 & 79.3 \\ YOLO v5s & 75.1 & 71.0 & 72.6 & 74.0 \\ YOLO v7-tiny & 66.9 & 66.5 & 66.7 & 65.5 \\ YOLO v8s & 74.4 & 75.6 & 75.0 & 77.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of overall accuracy with various models (%). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Models**} & **Number of** & **Training Duration** & **Memory** & **Video Memory** & **FPS (f\(\cdot\)s\({}^{-1}\))** \\  & **Parameters (\(\times\)10\({}^{6}\))** & **(h)** & **Consumption (MB)** & **Usage (GB)** & **FPS (f\(\cdot\)s\({}^{-1}\))** \\ \hline Faster-RCNN & 136.75 & 7.1 & 534.2 & 5.6 & 12.80 \\ YOLO v5s & 7.02 & 3.7 & 14.12 & 3.5 & 127.42 \\ YOLO v7-tiny & 6.01 & 3.8 & 12.01 & 1.9 & 82.56 \\ YOLO v6s & 11.13 & 3.1 & 21.98 & 3.6 & 125.74 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The results of running performance with various models. average precision, with YOLOv7-tiny exhibiting the lowest performance. Therefore, Faster-RCNN outperformed the other models in recognizing longitudinal cracks. 2. For transverse cracks (_TC_), the YOLOv8s model achieved a superior recognition accuracy with an AP score of 89.5%, followed by YOLOv5s. Although there was a slight decrease in F1 score for YOLOv8s compared to YOLOv5s, their overall recognition accuracies did not significantly differ from each other; however, YOLOv7-tiny displayed a weaker recognition accuracy. 3. All four algorithm models exhibit low recognition accuracy and F1 values for oblique cracks (_OC_) compared to the other types of cracks; however, among them, Faster-RCNN still maintained the highest level of recognition accuracy, while all models belonging to the YOLO series demonstrated lower levels of recognition accuracy--this explains why Faster-RCNN performed better overall. 4. In terms of recognizing mesh cracks (_AC_), an outstanding performance was observed from the YOLOv8s model, which attained a remarkable recognition accuracy and F1 value at 91.0% and 90.6%, respectively; meanwhile, although slightly less effective than its counterpart, the YOLOv5s model also showcased a commendable performance, whereas a poor performance was exhibited by the YOLOv7-tiny model. The Results of Detection Accuracy under Different Crack Datasets  
In this study, our self-made pavement crack datasets strictly followed the UAV flight parameter settings and data acquisition process mentioned in Section 4.