## 3 Data Description and Model Evaluation Index  
### Data Description  
To evaluate the performance of the proposed framework in enhancing cross-scene detection, a cross-scene pavement distress detection dataset was established, comprising pavement images from six countries, four different viewpoints, and multiple scenes. This dataset is divided into two parts: the standard dataset and the comprehensive dataset. The standard dataset, labeled as CHN_C, consists of images captured in simpler scenes, focusing on distress with a direct view of the pavement surface, and is used for model training. The CHN_C dataset was collected from over 3000 km of asphalt roads in Shanghai, using a high-definition (HD) camera mounted on the rear of a vehicle to capture road images at a resolution of 1628 \(\times\) 1236. All images were taken on sunny days with dry road surfaces. The comprehensive dataset comprises a diverse collection of pavement images from multiple scenes, sourced from the Crowdsensing-based Road Damage Detection Challenge (CRDDC2022) [30]. The CRDDC2022 dataset encompasses pavement images from six countries: Norway, Japan, India, the Czech Republic, the United States, and China. Images are derived from a variety of sources, including vehicle-mounted high-definition cameras, vehicle-mounted smartphones, Google Street View images, motorbikes, and drones. Due to the diversity in collection equipment, locations, and shooting perspectives, there are observable differences in background, viewpoint, and scene elements among the images across these datasets, as demonstrated in Figure 7. Table 1 presents the details of each pavement image dataset used in this study. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  
The CHN_C dataset was used as the source domain data for training the detection model due to its consistent and simplified image scenes. Vehicle-mounted cameras capture images at an oblique view, closely positioned to the pavement, contributing to small perspective distortion and high resolution. Furthermore, collection conditions on sunny days without water accumulation can reduce the impact of external interferences such as weather, lighting, and shadows, which enhance the model's accuracy in capturing pavement distress features. To balance the number of images in the training and test sets, we selected 10,000 images to serve as the training set, as detailed in Table 2.  
To validate the generalizability of the proposed model in cross-source pavement images, images from Norway, Japan, the United States, and China within the CRDDC2022 dataset were selected as the test set. Data from India and the Czech Republic were not chosen due to their similarity to the Japan dataset. In NO_C, JPN_S, and US_G datasets, pavement images were captured parallel to the road surface using vehicle-mounted cameras. Compared to the training set (CHN_C), their primary differences lie in the image background. Owing to variations in the construction of roads, buildings, and roadside \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & **Number of** & **Number of** & **Number of** & **Number of** & **Number of** \\  & **Longitudinal** & **Transverse** & **Alligator** & **Potholes** & **Images** \\  & **Crack** & **Crack** & **Crack** & **Selected** \\ \hline CHN\_C & 10,463 & 8782 & 6472 & 3247 & 10,000 \\ NO\_C & 2147 & 849 & 289 & 271 & 2000 \\ JPN\_S & 1275 & 1142 & 1052 & 863 & 2000 \\ US\_G & 2753 & 1475 & 357 & 58 & 2000 \\ CHN\_M & 2678 & 1096 & 641 & 235 & 1977 \\ CHN\_D & 1204 & 943 & 251 & 70 & 2000 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Sample statistics selected from each dataset. Figure 7: Pavement sample images of each dataset. (**a**) CHN_C. (**b**) NO_C. (**c**) JPN_S. (**d**) US_G. (**e**) CHN_M. (**f**) CHN_D. facilities across these countries, images in each dataset have a unique background. This diversity allows us to verify the generalization performance of the proposed model in the presence of background differences.