# Crack Segmentation on Earthen Heritage Site Surfaces  
Yuan Zhang  
School of Information Science and Technology, Northwest University, Xi'an 710127, China  
[PERSON]  
Correspondence: [EMAIL_ADDRESS] (Y.Z. ); [EMAIL_ADDRESS] (Z.Z.) Wu Zhao  
School of Information Science and Technology, Northwest University, Xi'an 710127, China  
Cracks comprise the damage to the main structures of earnthe heritage sites. Detecting the crack formation process can effectively predict the trend in the damage to earnthe sites and thus help officials adopt appropriate methods to maintain the sites' value. The traditional method of crack detection was measuring the length and width of the cracks through visual inspection by well-trained cultural relic workers with the help of rulers. Although the current state of the crack can be recorded via manual inspection, long-term observation of the crack and the objectivity and reliability of the crack observation cannot be effectively guaranteed. The replacement of observers will lead to the instability of observations. Therefore, many researchers focus on developing automated and efficient crack-detection techniques for the long-term monitoring of earnthe heritage sites. The application of computer vision-based crack detection technology for modern buildings, such as cement roads, brick walls, and bridges, has been widely studied. Threshold segmentation [6; 7], morphology [8], wavelet transform [9], and filter-based algorithms [7; 10; 11] have been applied in the field of crack detection. [PERSON] et al. [12] summarized traditional computer vision-based crack detection and subdivided them into four categories, namely an integrated algorithm, morphological approach, percolation-based method, and practical technique, and then analyzed and compared the experimental results of the four types of methods. Using these methods to analyze a photo of the building surface, one can identify the cracks, locate the cracks, and measure the cracks' width, length, and orientation. By adjusting the parameters of these algorithms, high inspection detection accuracy can be achieved. The traditional computer vision-based crack detection algorithm usually focuses on the understanding and analysis of the partitive features of the image, and parameters are designed for a specific dataset. However, when there is interference, such as stains, potholes, etc., the partitive image will be difficult to segment. The main characteristics of rammed earth heritage are that cracks are distributed on the irregular surface and mixed with irregular textures, such as shadows and holes. It is difficult for computer vision methods to segment cracks. Figure 1: Suoyang Ancient City. (**a**) Google Maps image of Suoyang Ancient City. The photos show (**b**) some typical damage to wall surfaces, including (**c**) scouring, (**d**) flaking, and (**e**) cracking. In order to overcome the drawbacks of traditional computer vision-based algorithms, some studies have suggested applying machine learning to image processing tasks. [PERSON] et al. first proposed that Convolutional Neural Networks (CNN) could be applied to handwritten character recognition [13]. More studies have proposed image classification [14; 15; 16], object detection [17; 18; 19], semantic segmentation [20; 21; 22], and crack detection [23; 24; 25] tasks. Instead of extracting features, Convolutional Neural Networks can automatically learn the features of an image and identify cracks from images. Deep learning semantic segmentation models have been extensively studied for crack detection in modern buildings with positive results. Classical machine learning algorithms, such as Support Vector Machines [26] and Random Forests [27], require the manual extraction of crack features. Even though this sort of algorithm relies heavily on manual extraction, they produce some fruitful study findings.