# Crack Segmentation on Earthen Heritage Site Surfaces
The accuracy of the algorithm will be diminished to varying degrees due to the variations in external environmental factors, such as background noises and lighting conditions, when extracting manually. These factors may lead to unexpected practical problems and limitations in the application of classical machine learning algorithms. As a branch of machine learning, the architecture of deep learning can extract features related to it and thus can effectively avoid the limitations existing in traditional algorithms. Research on crack detection technology based on deep learning, including image classification, object recognition, and semantic segmentation, has been proposed. Image classification algorithms can automatically classify the images into two categories, crack images and non-crack images. Object recognition algorithms can find and locate objects that contain cracks in images and use bounding boxes to identify and locate them. Semantic segmentation is basic research in the field of computer vision processing and has been widely used in many fields, such as autonomous driving, medical image analysis, etc. Compared with image classification and object recognition, semantic segmentation is more accurate in positioning, and its positioning output can be distinguished using different colors. It can identify the width and length of cracks at the pixel-level. Obviously, in crack detection for earthen heritage sites, it is important not only to detect whether there are cracks on the surface but also determine the size of the cracks, which is of great significance in studying the causes and development of the cracks and proposing scientific protection plans. [PERSON] et al. [28] used GoogLeNet and ResNet classification architecture to find crack patches, employed [PERSON]'s thresholding to segment crack patches, and adopted median filtering to eliminate the influence. [PERSON] et al. [29] proposed the faster region proposal convolutional neural network (Faster R-CNN) algorithm to detect crack regions. [PERSON] et al. [30] used Mask R-CNN to perform crack segmentation at the pixel-level. Several studies have employed two settings: first, the classification architecture was used to locate crack patches, and then, the crack map was extracted at the pixel-level. The approach above is known as Hybrid Semantic Segmentation. Another method was performed with encoder-decoder structures without identifying a crack patch or crack region beforehand. A typical encoder-decoder structure consists of an encoder network and a corresponding decoder network. The encoder network has a backbone architecture that contains convolution, pooling, and activation layers. The role of the decoder network is to map the low-resolution feature maps to full-resolution feature maps for pixel-wise classification. Several crack segmentation methods with encoder-decoder structures have been developed to detect surface cracks, such as U-Net [31], SegNet [32; 33], LinkNet [34], ResNet [35], and several variants [36; 37]. In the semantic segmentation setting, the key to achieving improved accuracy is to provide the contextual information flow in architecture [38]. U-Net was initially released in the context of medical image analysis for biomedical image segmentation [31]. In several studies, the U-Net architecture has been proposed for crack detection. [PERSON] et al. [39] used morphological filtering to improve the binary map with localized cracks from the U-Net neural network. [PERSON] et al. [40] put forward a U-Net-based architecture that replaces the encoder with a pre-trained ResNet-34 backbone. [PERSON] et al. [41] advanced a U-Net architecture that employs the Full Attention Strategy, which comprises synthesis of the attention mechanism and the outputs from each encoding layer in a skip connection. [PERSON] et al. [42] used the squeeze-and-excite module to empower U-Net, which they call SE-U-Net. This architecture can adaptively learn the shallow information matched with deep features in the skip path.