## 4 Experimental Details
### Datasets  
The contents of SVRDD and MS COCO datasets are shown in Table 1, and a detailed explanation of each dataset is carried out below.  
(1) The Street View Image Dataset for Automated Road Damage Detection (SVRDD) [54] is a dataset designed for road damage detection using street view images. These dataset is  
\begin{table}
\begin{tabular}{c c c c} \hline
**Data Set Name** & **Experimental Image Size** & **Number of Categories** & **Number of Pictures** \\ \hline SVRDD & 640 \(\times\) 640 & 7 & 8000 \\ MS COCO2017 & 80 & 123,287 \\ \hline Data set name & Number of frames & Number of samples in the training set & Test set sample size \\ \hline SVRDD & 20,804 & 6000 & 1000 \\ MS COCO2017 & 886,000 & 118,287 & 5000 \\ \hline \end{tabular}
\end{table}
Table 1: Details of SVRDD and MS COCO datasets.  
the first publicly available street view image dataset dedicated to road damage detection, and consists of 8000 street view images from the Dongcheng District, Xicheng District, Haidian District, Chaoyang District, and Fengtai District in Beijing. These images cover a wide range of urban road types and pavement conditions with detailed annotations, and contain a total of 20,804 damage instances with bounding box annotation levels. Seven categories of pavement damage were included: longitudinal cracks, transverse cracks, web cracks, potholes, longitudinal patches, transverse patches, and manhole covers. The images in the dataset were captured in multiple seasons, weather and lighting conditions, and the background contained pedestrians, vehicles, buildings, viaducts, trees and their shadows, adding to the challenge of detection. Detailed analysis of the dataset is shown in Figure 5. The SVRDD dataset consists of 8000 images with a total of 20,804 damage annotations across six categories. Potholes and alligator cracks are less common, as they typically occur after severe road aging. Among the five regions, Chaoyang District has the lowest average number of damage instances. Damage centers are mainly located in the lower half of the images, while the upper half generally serves as the background. Due to perspective effects, the road areas in the upper half are narrower, making damage detection more challenging. The height-width distribution shows that damage widths can span the entire image, whereas lengths reach up to half the image height. Longitudinal and transverse cracks and patches are similarly distributed, occupying up to 10% of the image area. Alligator cracks cover larger areas, up to 50%, while potholes and manhole covers occupy mostly less than 0.5% of the image area.  
(2) The Microsoft Common Objects in Context (MS COCO) dataset [55] is a crucial benchmark in the field of computer vision, widely utilized for tasks such as object detection, image segmentation, and image annotation. The COCO dataset encompasses 80 common object classes across more than 330,000 images, with approximately 200,000 images providing detailed annotation information. These annotations include object bounding boxes, pixel-level segmentation masks, human keypoints, and image descriptions. Due to its complex scenarios, diverse annotation types, and high-quality data, COCO has become a standard platform for evaluating the performance of deep learning models, and has significantly contributed to the advancement of computer vision technology.  
Figure 5: Visualization of detailed analysis of the SVRDD dataset.