## 4 Results and Analysis
### Ablation Experiments
This led to an increase in mAP by 5.41%, Recall by 6.56%, and F1-score by 7.42%. The addition of the adaptive normalization module weakened the distribution bias between the train and test datasets to some extent, ensuring better adaptation of the model to the test dataset. This resulted in an increase in mAP by 4.4%, Recall by 6.6%, and F1-score by 7.4%. Moreover, the combination of these modules further enhanced the model's detection capabilities. These three modules address different adaptation challenges encountered when the model is transferred to the test dataset, proposing targeted improvements from three aspects, thereby further enhancing the models' pavement distress detection capabilities. Compared to the original object detection model, the test-time adaptation model showed improvements in Precision by 28.0%, mAP by 20.38%, Recall by 18.23%, and F1-score by 22.92%, demonstrating superior performance in all aspects. However, it must be acknowledged that despite proposing various modules to address the generalization issues during model transfer, the performance is still inferior to direct training and testing on the test dataset. This is because when training on and testing the test set, the object detection model encounters data during the evaluation phase that it has already seen and fully adapted to, resulting in higher detection accuracy. However, obtaining data and labels from the target domain is not always feasible and requires significant human and material resources for data extraction and labeling. Therefore, it is not always practical to retrain and fine-tune the pavement distress detection model on data from the new target domain every time it is transferred. Contrary to this approach, the framework proposed in this study only requires obtaining a few images from the target domain to adapt the model, which can significantly improve the accuracy of pavement distress detection models. Therefore, the framework proposed in this study has significant practical value as it reduces the resource-intensive nature of retraining on target domain data and still achieves notable improvements in model accuracy. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Precision/\%** & **Recall/\%** & **mAP/\%** & **F1/\%** \\ \hline YOLO & 42.14 & 44.24 & 39.86 & 43.16 \\ YOLO + VT & 54.38 & 51.30 & 48.45 & 52.80 \\ YOLO + FF & 50.36 & 50.80 & 45.27 & 50.58 \\ YOLO + AN & 50.29 & 50.84 & 44.26 & 50.56 \\ YOLO + VT + FF & 65.82 & 57.02 & 55.35 & 61.10 \\ YOLO + VT + AN & 66.26 & 53.78 & 49.34 & 59.37 \\ YOLO + FF + AN & 62.27 & 54.85 & 48.30 & 58.32 \\ YOLO + VT + FF + AN & 70.14 & 62.47 & 60.24 & 66.08 \\ \hline Transfer-YOLO & 75.34 & 68.89 & 70.35 & 71.97 \\ \hline FasterRCNN & 44.39 & 46.05 & 43.02 & 45.20 \\ FasterRCNN + VT & 54.61 & 57.47 & 52.27 & 56.00 \\ FasterRCNN + FF & 49.93 & 52.85 & 47.53 & 51.35 \\ FasterRCNN + AN & 50.34 & 51.37 & 47.02 & 50.85 \\ FasterRCNN + VT + FF & 65.86 & 63.29 & 58.68 & 64.55 \\ FasterRCNN + VT + AN & 66.37 & 64.63 & 55.26 & 65.49 \\ FasterRCNN + FF + AN & 63.65 & 60.26 & 54.18 & 61.91 \\ FasterRCNN + VT + FF + AN & 71.10 & 67.17 & 64.10 & 69.08 \\ \hline Transfer-FasterRCNN & 76.26 & 71.23 & 68.57 & 73.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The evaluation results of ablation experiments.