# Automated Pixel-Level Deep Crack Segmentation on Historical Surfaces Using U-Net Models
## 1 Introduction  
For historical buildings and objects that are subject to various damages, cracks represent the most critical type of damage [1, 2, 3]. However, the task of inspecting the structural conditions of historical surfaces is carried out manually in most cases, which is a costly and subjective process. Thus, developing automated and efficient structural damage identification techniques has attracted the attention of many researchers. The processes of crack detection and analysis are of great significance, as indicated by the importance of cracks as an indicator of buildings' safety and durability factors [4]. Most structures, such as bridges, roads, and buildings, become susceptible to danger and cracking due to the periodical loading, weather, or stress accumulation [5]. Consequently, continuous structural health monitoring can be a very laborious task for humans, while it is easy for automated systems based on computer vision. Thus, the demand for building automatic crack detection systems has rapidly increased, despite the challenges that theyface due to complex real environments, illumination conditions, and the irregular shape of cracks [6]. Pixel-level crack detection in historical building images is a challenging task because of many sources of uncertainty, such as shadows, ornaments, carvings, separators, crack-like artifacts, and wood patterns [6]. For the case of crack detection from images, the user only provides different photos as input and receives as output any detected cracks in these photos, without the necessity for any manual intervention. The task of semantic segmentation is to assign each pixel of an image a class label. Manually performing this task is time-consuming, error-prone, and resource-exhaustive, as some cracks may be only one pixel wide. Pixel-level crack detection can be used to perform quantitative analysis on crack width and length to assess the degree of crack severity. Practically, such assessment is effectively used for diagnosing the integrity of a building's infrastructure, such as bridges, pavements, and other buildings, using image sensors. Historical buildings have various surface structures, dust, ornaments, carvings, wood patterns, separators, corrosion on walls, bird droppings, fungi, detachment, color degradation, other damages, and weathering damages that hinder the ability to recognize the correct abnormality--namely, cracks. As shown in Figure 1, surface images include corrosion, shadows, as well as different appearances, and textures. In Figure 1a,b, corrosion and wood patterns are shown, respectively. On the other hand, Figure 1c,d,h contain different texture patterns, while carvings, separators, and bird droppings are shown in Figure 1e,f,g, respectively. This research focuses on utilizing deep learned features for the pixel-level crack detection/segmentation problem. Cracks usually appear on structures as the first visible sign of structural damage. In the last few decades, as a response to the crack segmentation problem, various traditional computer vision-based segmentation approaches, including thresholding, the modified Tubularity Flow Field (TuFF) algorithm, filtering, tensor voting, super-pixel methods, morphological approaches, clustering, and skeletonization techniques, have been introduced in the literature to perform pixel-level crack detection in the images of engineering structures. Moreover, several deep learning-based crack segmentation methods have been widely developed to efficiently detect surface cracks, such as DeepCrack, SegNet, SDDNet, and U-Net [7; 8; 9; 10; 11]. The significance of this paper is to investigate the impact of utilizing pixel-Level deep crack segmentation through adopting several variants of the U-Net deep learning model for crack detection on historical surfaces. The main contributions of this paper are summarized as follows:  
Figure 1: Historical building surface examples.