# TMCrack-Net: A U-Shaped Network with a Feature Pyramid and Transformer for Mural Crack Segmentation
## 1 Introduction  
Ancient murals are regarded as valuable cultural heritage, reflecting the social landscape of the times they are from. They provide a valuable basis for studying ancient cultures because of their scientific, historical, and humanistic value. However, under the influence of natural and artificial factors, many murals [1] have suffered from problems such as cracks, puckering, and scratches, which weaken the information expression of frescoes and are not conducive to preserving history and culture, or the transmission of the art. Screen cleaning, reinforcement, paper, cloth stickers, disease analysis, expert evaluation, and disease labeling [2] are all involved in the manual disease labeling of ancient murals. A manual method will not only cause secondary damage to the murals but will also be very inefficient. Therefore, image processing technology must be introduced to help heritage conservationists protect murals. Accurate segmentation of cracks in mural images is challenging, and mural paintings have complex background structures. The painting of the polo picture in the tomb of Prince [PERSON] in Tang Dynasty is rich in content; the painting has different backgrounds of people, horses, trees among mountains and rocks; and the complex image content increases the difficulty of crack recognition. Cracked images are shown in Figure 1.  
Among the crack detection algorithms of traditional image processing, [PERSON] proposed a K-means Sobel algorithm to extract the disease edges of murals. Two evaluation [3] criteria were given for the specificity of murals: disease recognition rate and edge continuity. Subsequently, his team [4] used geographic information system technology (GIS) to produce high-precision digital orthophoto maps (DOM) of murals, establish a spatial database of diseases, obtain disease maps and trend maps, obtain the locations and severity of diseases through spatial analysis and spatial statistics, and use decision trees to classify disease classes. [PERSON] [5] applied data mining techniques to image segmentation techniques to extract crack information with unsatisfactory results. [PERSON] [6] proposed a multi-scale high-hat transform and K-SVD method and a post-processing method based on semi-supervised clustering to eliminate mislabeling, and weighted fusion of the three methods was performed to calibrate crack information of different sizes and brightnesses. Two types of methods are described below: (1) manual depiction using GIS software; (2) image processing using edge detection and multivariate filtering. Though both methods involve non-contact extraction, they are mostly human-computer interactions and semi-automatic methods requiring improved accuracy. With the development of computational vision and deep learning, different neural networks are used for mural painting disease detection. [PERSON] [7] used hyperspectral images for disease region identification, using minimum noise fraction (MNF) to focus on different bands and reduce the effect of noise on the data. The mural images were classified into several types of damaged areas and normal areas by back propagation (BP) neural network. [PERSON] [8] used a U-Net network with multi-scale details to detect paint peeling disease in mural paintings of Fengguo Temple of Liao Dynasty in Yi County, Jinzhou City, China. The shallow features of the wall paintings were injected into different stages of the encoder to obtain detailed information about the paint peeling disease, and attention mechanisms were added to suppress invalid features in the encoding stage. [PERSON] [9] proposed the Ghost-C3 SE YOLOv5 network to detect the damage to cave murals. The YOLOv5 network structure was adjusted to reduce the dimensionality of the convolutional layers. An attention mechanism was added to the backbone network to adjust the importance of different feature channels. This paper proposes the TMCrack-Net model using the ConvNext network as the encoder skeleton.