## 2 Dataset Scene Standardization Analysis  
As the information carrier of infrastructure cracks, crack image datasets are prerequisite for white-box and black-box methods based on crack image segmentation and are of great importance to researchers [14; 15; 16; 17; 18; 19; 20; 21; 22]. Therefore, a series of crack image datasets of different materials, including concrete, pavement asphalt, and metals, were designed and produced. Although a number of public infrastructure crack image datasets were released, the image capture devices, infrastructure materials, image sizes, image numbers, and imaging environments of popular datasets are totally different, as list in Table 1, which indicates that there is no standard rule for the selection of above factors.  
Image capture devices could be mainly categorized into three classes including hand-held, vehicle-carried, and unmanned aerial vehicle-carried camera or laser radar [14], and the type and resolution of capture devices are also different from each other. For example, the capture devices of current public datasets in Table 1 are, respectively, an iPhone5 [15], a mobile phone [20; 22], an area-array camera [18], and a 2D laser [19; 21]. The classification hierarchy of these devices is very coarse and indistinct, which indicates that there is no standard for crack capture device selection. It can also be seen that the resolution and image number of each dataset are different, indicating non-standardization in the image size and number of datasets.  
Moreover, there are non-standardization problems in the image capture environment, image sources, and image labeling in current public infrastructure crack image datasets. In the dataset list in Table 1, only images in Crack3238 [7], DeepCrack [17], and TITS [19] are high quality labeled, and there are mislabeling and incorrectness in other datasets. The images in Figure 1 are typical non-standardization image samples from the datasets in Table 1. In Figure 1a,b, the object building captured is too large, reasonable spatial resolution could not be used, and there are unnecessary background images in the visual field in the image, for example, the sky or unrelated buildings and objects. In Figure 1c, the watermarked pavement crack image is from a network with an unrecognized spatial resolution and capture device, and in Figure 1d, the out-of-focus pictures may have be captured due to operational issues, such as shaking of the acquisition equipment, improper shutter speed, and incorrect aperture. In Figure 1e,f, certain cracks are left unlabeled due to overexposure.  
In the theory of photography, image quality is greatly influenced by camera type, image capture scene parameters including surrounding illumination intensity, wavelength and angle, and image capture parameters such as shooting angle, shutter speed, and aperture size. Insight into the photoelectric process of concrete crack image capture is necessary to explore crack imaging scene standardization.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Dataset** & **Materials** & **Quantity** & **Resolution** & **Device** \\ \hline DeepCrack [17] & Hybrid & 537 & 544 \(\times\) 364 & - \\ CFD [15] & Pavement & 320 & 480 \(\times\) 320 & iPhone5 \\ CrackTree [18] & Pavement & 206 & 600 \(\times\) 800 & Area-array camera \\ TITS [19] & Pavement & 269 & Multi & 2D laser \\ Crack500 [20] & Pavement & 500 & 360 \(\times\) 640 & Mobile phone \\ GAP8384 [21] & Pavement & 384 & 1920 \(\times\) 1080 & 2D laser \\ Crack3238 [7] & Hybrid & 3238 & 256 \(\times\) 256 & - \\ crack dataset [22] & Concrete & 776 & 297 \(\times\) 306 & Mobile phone \\ \hline \end{tabular}
\end{table}
Table 1: Public crack segmentation datasets.