## 4 Results and Analysis
### Ablation Experiments  
To validate the effectiveness of the three modules proposed in this Adaptive Pavement Distress Detection Framework, this study conducted an ablation experiment. Figure 9 illustrates the recognition results of the original YOLO model, YOLO + viewpoint transformation (VT), YOLO + foreground focus (FF), YOLO + adaptive normalization (AN), YOLO + VT + FF, YOLO + VT + AN, YOLO + FF + AN, YOLO + VT + FF + AN, FasterRCNN model, FasterRCNN + VT, FasterRCNN + FF, FasterRCNN + AN, FasterRCNN + VT + FF, FasterRCNN + VT + AN, FasterRCNN + FF + AN, FasterRCNN + VT + FF + AN. Overall, the individual use of each module can highlight the features of cracks in the image to a certain extent, thereby improving the model's accuracy in recognizing pavement distress. Furthermore, the combination of these modules enhances the representation features of distress in the images. This indicates that the stacking of these modules contributes to improving the model's generalization ability in cross-domain recognition, consequently enhancing the model's detection accuracy. To quantitatively assess the contributions of each module to cross-domain recognition of pavement distress, this study trained various models on standard datasets and performed predictions on a comprehensive test dataset, as shown in Table 5. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Distress Types**} & \multicolumn{4}{c}{**YOLO**} & \multicolumn{4}{c}{**FasterRCNN**} \\ \cline{2-9}  & **Precision/\%** & **Recall/\%** & **AP/\%** & **F1/\%** & **Precision/\%** & **Recall/\%** & **AP/\%** & **F1/\%** \\ \hline Longitudinal Crack & 35.14 & 38.26 & 30.25 & 36.63 & 40.18 & 45.46 & 40.27 & 42.66 \\ Transverse Crack & 32.23 & 37.18 & 30.85 & 34.53 & 42.34 & 45.24 & 40.64 & 45.02 \\ Alligator Crack & 70.28 & 77.23 & 72.43 & 73.59 & 55.78 & 60.28 & 55.98 & 59.27 \\ Pothole & 30.91 & 24.29 & 25.91 & 27.20 & 39.24 & 33.23 & 35.17 & 30.36 \\ \hline Average & 42.14 & 44.24 & 39.86 & 43.16 & 44.39 & 46.05 & 43.02 & 44.51 \\ \hline \multicolumn{9}{c}{**TTA-YOLO**} & \multicolumn{4}{c}{**TTA-FasterRCNN**} \\ \cline{2-9}  & **Precision/\%** & **Recall/\%** & **AP/\%** & **F1/\%** & **Precision/\%** & **Recall/\%** & **AP/\%** & **F1/\%** \\ \hline Longitudinal Crack & 66.24 & 59.28 & 57.32 & 62.57 & 67.13 & 58.37 & 64.39 & 62.44 \\ Transverse Crack & 60.18 & 58.46 & 57.80 & 59.31 & 63.74 & 62.31 & 60.42 & 63.02 \\ Alligator Crack & 83.12 & 80.54 & 80.88 & 81.81 & 80.26 & 82.54 & 77.35 & 81.38 \\ Pothole & 71.02 & 51.6 & 44.96 & 59.77 & 73.27 & 65.46 & 54.23 & 69.15 \\ \hline Average & 70.14 & 62.47 & 60.24 & 66.08 & 71.10 & 67.17 & 64.10 & 69.08 \\ \hline \multicolumn{9}{c}{**Transfer-YOLO**} & \multicolumn{4}{c}{**Transfer-FasterRCNN**} \\ \cline{2-9}  & **Precision/\%** & **Recall/\%** & **AP/\%** & **F1/\%** & **Precision/\%** & **Recall/\%** & **AP/\%** & **F1/\%** \\ \hline Longitudinal Crack & 70.48 & 63.18 & 60.52 & 66.63 & 71.23 & 59.85 & 65.41 & 65.05 \\ Transverse Crack & 67.36 & 65.24 & 62.84 & 66.28 & 68.52 & 65.42 & 68.50 & 66.93 \\ Alligator Crack & 85.38 & 82.62 & 86.31 & 83.98 & 85.94 & 84.37 & 88.93 & 85.15 \\ Pothole & 78.46 & 54.08 & 45.81 & 64.03 & 79.35 & 75.28 & 51.44 & 77.26 \\ \hline Average & 75.34 & 68.89 & 70.35 & 71.97 & 76.26 & 71.23 & 68.57 & 73.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of validity experiments. Figure 9: Detection result of ablation experiments. From Table 5, it can be observed that integrating the viewpoint transformation module into the original object detection models helps the models adapt to pavement distress images from different viewpoints in the test dataset, thereby enhancing the models' detection capabilities. This resulted in an increase in mAP by 8.59%, Recall by 7.06%, and F1-score by 9.64%. Incorporating the foreground focus attention module allows the model to focus more on distinct features of distress while ignoring background interference, leading to improved detection capabilities.