## 5 Experiments and Results
### Experimental Results
#### 5.4.2 Comparison Results of Detection Accuracy
To validate the reliability and advantages of our self-made crack datasets, we conducted a comparative study using these four model algorithms on existing various open-source UAV pavement crack datasets. Our experiment involved comparing the detection accuracy of our crack datasets with datasets such as UAPD [2], RDD2022 [31], UMSC [19], UAVRoadCrack [21], and CrackForest [32]. We evaluated and compared the accuracy performances of Faster-RCNN, YOLOv5, YOLOv7-tiny, and YOLOv8s after 200 training cycles, as well as Faster-RCNN after 15 rounds. The results, as presented in Table 6, indicate that our lab's datasets outperformed other datasets used in similar models on most metrics, exhibiting the highest accuracy for crack recognition and algorithmic efficiency. However, the model performance varied across different datasets; while UAVRoadCrack performed relatively well, the UAPD dataset showed the worst performance. These findings strongly highlight the advantages of utilizing our self-collected pavement images via a UAV and emphasize the importance of flight parameter modeling for the quality control of UAV imagery. The Results of Detection Effectiveness  
To facilitate a more intuitive comparison of the effects, a specific image with four types of cracks was selected from the test set to evaluate and compare their recognition performances, as presented in Table 7. Based on the results obtained, it is evident that Faster-RCNN outperformed the YOLO series algorithms in terms of overall performance. It is worth noting that, for challenging oblique cracks (_OC_), all the YOLO series algorithms exhibited unsatisfactory recognition with low confidence levels, often resulting in the omission or separate identification of complete cracks, whereas the Faster-RCNN model demonstrated a superior capability in recognizing oblique cracks (_OC_) more comprehensively. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{4}{c}{_AP_ (\%)} & \multicolumn{4}{c}{_F1-Score_} \\ \cline{2-9}  & **TC** & **LC** & **AC** & **OC** & **TC** & **LC** & **AC** & **OC** \\ \hline Faster-RCNN & 85.7 & 83.4 & 60.2 & 87.8 & 82.3 & 78.0 & 58.1 & 82.9 \\ YOLO v5s & 75.5 & 87.4 & 43.8 & 89.1 & 72.3 & 86.5 & 43.5 & 88.0 \\ YOLO v7-tiny & 70.4 & 81.2 & 40.7 & 80.7 & 70.0 & 79.0 & 44.8 & 77.1 \\ YOLO v8s & 75.4 & 89.5 & 45.4 & 91.0 & 74.4 & 85.0 & 48.5 & 90.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of detection accuracy with various models under four crack types (%). sively. Additionally, the Faster-RCNN model also exhibited an excellent performance in detecting subtle cracks, as shown in Table 7. For instance, it successfully identified a subtle transverse crack within a longitudinal crack. In other crack types, all four modeling algorithms demonstrated effectiveness in crack detection. A comparative analysis considering both combined effects and confidence levels reveals that Faster-RCNN achieved the best overall performance; among the YOLO series algorithms, YOLOv5s and YOLOv8s showed comparable results, while YOLOv7-tiny performed relatively poorly, with lower confidence levels observed across all detected results.