# Ceramic Cracks Segmentation with Deep Learning
## 1 Introduction  
In civil construction, buildings must be able to withstand the action of degradation agents for a predetermined or predicted time [1]. The building's facades include the cladding system that serves to protect the building from external degradation agents, in addition to providing functional and aesthetic comfort to its users [2]. Pathological manifestations are common at these points, and they occur more frequently in ceramic materials, which are used on a large scale in buildings. Besides, these manifestations arise in other types of materials, such as mortar and stone. They can be related to several factors such as excessive load, humidity variation, thermal variation, biological agents, material incompatibility, and atmospheric agents [3]. These manifestations compromise the essential function of protection, which aims to protect the coated surfaces against the agents that cause deterioration that can present themselves in different ways. Thus, the consequences can range from aesthetic problems or performance of coating to risks of accidents with people, substantially aggravated by the height of the buildings [4]. The main types of pathological manifestations associated with ceramic facade coverings are cracks, efflorescence, detachment, and those resulting from biological processes. Among these, the fissure is the most found in the literature since it compromises the building safety, puts at risk the people that travel around it, and presents a more critical aesthetic aspect [3, 5, 6, 7]. A fissure's main characteristic is the rupture appearance on the ceramic plate surface or body, causing the loss of the facade's integrity and uncovers some of its components, the plates, or joints. When the fissure happens, a detachment of the substrate plate is generated [4]. Image processing techniques (IPTs) are currently applied in civil engineering for images collected from inspections. These techniques emerged to detect cracks in the civil infrastructure, partially reducing the work done by human beings, and used several imageprocessing techniques to extract characteristics of cracks in the surfaces of the images [8]. However, many structure analyses and inspections are carried out manually, and this requires a lot of knowledge, experience, and time from those who will perform this activity, thus making the activity long and time-consuming. Automatic crack detection is essential in places that are difficult to access due to height or scale, to avoid exposing people to dangerous situations, and to speed up the inspection process [9]. On the other hand, applying procedures takes time due to the complexity of the work performed, including the installation of scaffolding, observation of a large area, and even the use of elevators or [PERSON]'s chair. Therefore, to create an automatic crack detection solution in ceramics, we focus on an image segmentation methodology. Such methodology includes a pre-processing step because many ceramics have textures in opposition to concrete that has linearity in its texture. The proposed methodology also includes a deep learning model to solve the problem of crack detection in ceramics, which in the future may be coupled to drones to carry out these inspections in a less manual, faster, and less dependent on human action with specific knowledge for the area. This solution also allows the location of the crack to be identified by means of the segmented image, since the crack is segmented, showing its exact location, because in facade inspections, in addition to identifying that there is a crack, which other works do through classification, it is important to know where it is located. We can generate an overlay of the images, highlighting where the crack is for future analysis through segmentation. We created a database to implement the segmentation models that contain images of defective ceramic plates and the basic crack truth in each image. In summary, this work has as a novelty the detection of cracks through segmentation.