## 4 Example Verification  
### Experimental Dataset  
We created a dataset of apparent damage to concrete bridges, consisting of 4794 images. Domain experts annotated the dataset, which we used to evaluate the effectiveness of the proposed method in identifying apparent damage to concrete bridges. According to the regulations of China's road and bridge maintenance standards and inspection standards, apparent concrete damage was classified into seven types: cracks, spalling, honeycomb, exposed reinforcement, water seepage, and voids. The constructed dataset included at least one type of damage in each image. Augmenting the dataset enhances its diversity and richness, making the model's detection more effective. The original dataset was randomly divided into training, validation, and testing sets with a ratio of 8:1:1. Data augmentation techniques, such as flipping, rotation, and HSV (hue, saturation, and value) enhancement, were then applied to the divided dataset. The dataset sizes were as follows: 14,528 images in the training set, 1816 images in the validation set, and 1816 images in the testing set. Table 1 shows the statistical results of the number of annotated boxes for each type of damage. The images of the bridge's apparent damage collected in the experimental dataset were affected by the lighting environment, resulting in variations in image quality. Statistical analysis was conducted on the grayscale histograms of the images, which allowed for the categorization of the images into four lighting conditions:  
(1) Well-lit images, which exhibit high clarity and rich details. The histogram of the grayscale image displays a unimodal distribution, with grayscale values primarily ranging from 150 to 250. The average grayscale value of the image is greater than 170. (2) Partial shadow or occlusion images: The grayscale distribution is complex, with areas of varying brightness. The histogram of the grayscale image displays a bimodal distribution, with grayscale values primarily ranging from 50 to 100 and from 150 to 250. The image's average grayscale value is approximately 150. (3) Low-lighting images: The overall low brightness can result in blurry or confusing areas in the apparent damaged regions. The grayscale histogram of the image displays a unimodal distribution, with grayscale values primarily distributed between 50 and 100. The image's average grayscale value is approximately 100. (4) Dark-lighting images: The overall low brightness can make it challenging to distinguish details of the apparent damage. The image's grayscale histogram displays a unimodal distribution, with grayscale values ranging from 0 to 50. The average grayscale value of the image is below 30. Figure 9 shows the grayscale histograms for the four distinct illumination conditions. Table 2 presents a statistical study of the number of well-lit images, partial shadow or occlusion images, low-lighting images, and dark-lighting images. Figure 10 illustrates some examples of picture data. \begin{table}
\begin{tabular}{c c c c} \hline
**Labels (Damage)** & **Number** & **Labels (Damage)** & **Number** \\ \hline \hline \multirow{2}{*}{liefeng (crack)} & 17,636 & shenshui (seepage) & 9244 \\  & 11,875 & fengwo (comb surface) & 8330 \\  & 7082 & lujin (steel exposed) & 6584 \\  & 8274 & & \\ \hline \end{tabular}
\end{table} 
Table 1: Number of Labels for Each Damage in the Dataset. Figure 9: Images and grayscale histograms captured under varying lighting conditions. (**a**) Well-lit image and its gray histogram; (**b**) partial shadow or occlusion image and its gray histogram; (**c**) low-lighting image and its gray histogram; (**d**) dark-lighting image and its gray histogram. Figure 10: Partial images of the training set. (**a**) Examples of well-lit images; (**b**) examples of partial shadow or occlusion images; (**c**) examples of low-lighting images; (**d**) examples of dark-lighting images.