## 5 Conclusions  
This study proposes a TTA-based framework to enhance cross-scene pavement distress detection. The framework leverages a domain prior knowledge generator to facilitate \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Precision/\%** & **Recall/\%** & **mAP/\%** & **F1/\%** & **Time/s** \\ \hline YOLO & 42.14 & 44.24 & 39.86 & 43.16 & 0.0003 \\ FasterRCNN & 44.39 & 46.05 & 43.02 & 45.20 & 0.2500 \\ CycleGAN & 52.16 & 53.34 & 52.17 & 52.74 & 0.0150 \\ EasyTL & 50.56 & 50.19 & 48.27 & 50.37 & 0.0082 \\ DDTCDR & 38.27 & 36.34 & 34.29 & 37.28 & 0.0075 \\ GraftNet & 33.95 & 33.64 & 34.25 & 33.79 & 0.0086 \\ TTA-YOLO & 70.14 & 62.47 & 60.24 & 66.08 & 0.0230 \\ TTA-FasterRCNN & 71.10 & 67.17 & 64.10 & 69.08 & 0.4300 \\ Transfer-YOLO & 75.34 & 68.89 & 70.35 & 71.97 & 0.0003 \\ Transfer-RCNN & 76.26 & 71.23 & 68.57 & 73.66 & 0.2500 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The evaluation results of comparative experiments. unsupervised feature transfer from the source to target domains. To address challenges like background variations, viewpoint discrepancies, and distribution shifts, the framework incorporates three key modules: viewpoint conversion, foreground focus, and adaptive normalization. By enabling real-time adaptation to new data without requiring additional labeled data, this framework significantly improves the model's generalization ability and robustness in diverse scenarios. With the framework proposed in this paper, the detection accuracy of the two baseline models improved by 20.38% and 21.08% in terms of mAP, respectively. Notably, the framework maintained strong performance, particularly in detecting small-sized potholes. It is important to highlight that these improvements were achieved without requiring new scene annotation datasets. Additionally, the ablation experiment results show that the three modules--viewpoint transformation, foreground focus, and adaptive normalization--each made a significant contribution to accuracy, increasing mAP by 8.59%, 5.41%, and 4.4%, respectively. Among these, viewpoint transformation provided the largest improvement, indicating that viewpoint differences are a key challenge in cross-scene applications. Compared to the supervised transfer learning method, the unsupervised TTA achieves only a 6% lower comprehensive detection accuracy but reduces the need for 9977 high-quality annotated data points. Future research could focus on integrating additional distress and challenging datasets to further test and enhance the robustness of the model. Additionally, exploring the model's applicability beyond pavement distress detection--such as for other types of infrastructure condition assessments--could broaden its utility and impact. The extent to which the resolution of the collected images affects the accuracy of pavement damage detection will also be explored. Exploring more effective ways to integrate detection models from the source and target domains, rather than solely relying on data collection, represents a more promising direction for future research. Conceptualization, Y.L. ; methodology, Y.L. and D.W.; validation, M.D. and J.Y. ; data curation, Y.H., M.D. and J.Y. ; writing--original draft preparation, Y.L., Y.H. and L.L. ; writing--review and editing, Y.L. ; visualization, L.L. ; supervision, Y.H. ; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the Young Scientists Fund of the National Natural Science Foundation of China (Grant No. 52402387) and the China Postdoctoral Science Foundation (Certificate Number: 2024M/52425). Not applicable. The data used to support the findings of this study are available from the corresponding author upon request. Authors [PERSON] and [PERSON] were employed by the company Chongqing Traffic Engineering Quality Inspection Co., Ltd.