# Ceramic Cracks Segmentation with Deep Learning
## 2 Theoretical Foundation  
Image segmentation is a process that aims to divide images into regions or objects of interest that are homogeneous. This activity is the initial step in image processing applications, such as pattern recognition and image analysis. Image analysis includes characterization and representation of objects and measurement of resources. This process is mainly used to find objects and shapes [10]. There are currently several types of segmentation, usually based on formats, pixel characteristics, histograms, and movement. Each type supports common features in pixels or a group of pixels. [10; 11]. The evolution of deep learning to various kinds of computer vision problems in the literature encourages this work to build a crack segmentation model based on deep learning. IPTs had a significant advance in the last years. However, other problems have no solution found by IPTs yet, such as the real world's perception (lack of context of images, images with shadow, textures, variation in lighting), shading, and lighting variation. In parallel to this, there has been an exponential growth in the use of convolutional neural networks (CNN), and they have obtained better results for these problems. CNNs, too, have been used to classify cracks and fissures [8; 9; 12; 13; 14; 15]. However, none of the proposed CNNs deals specifically with coating ceramics. There are several studies in the literature on crack detection [8; 9; 11; 12; 16; 17; 18], but regarding cracking in ceramics there are few works and each surface has its specificity. [PERSON] in [8] applied a vision-based method using a deep architecture of convolutional neural networks (CNNs) to detect concrete cracks without calculations as defect characteristics. He aimed to create a model that could solve the problem without the use of processing image techniques. Moreover, [PERSON] compared the obtained results with traditional methods based on edge detection. CNNs obtained the best performance, but the work is not related to identifying the location of the crack in the image, but in an evaluation of an intact or cracked part, which for a facade inspection is not ideal, and it is necessary to identify where the crack is located. In this work, it was resolved using segmentation, managing to extract the exact location of the fissure  
In another study, [PERSON] [12] aimed to increase the level of automation in the inspection of concrete infrastructure when combined with unmanned aerial vehicles. The crack detection model developed is based on an image classification algorithm of the deep learning convolutional neural network (CNN). A relatively heterogeneous dataset has been provided. The authors claimed that deep learning allows the development of a concrete crack detection system responsible for several conditions, such as different light, surface finish, and humidity that a concrete surface can display. In this work, the model VGG16 [19] was used as a backend to the transference-learning technique. [PERSON]'s best experiment produced a model with an accuracy of 92.27%. However, [PERSON]'s work deals with image classification, stating whether or not there are cracks in concrete structures. Moreover, it does not make clear where the crack is located, which is essential for automated inspection of structures. In [PERSON] [18], the authors presented a new approach in image processing to detect cracks in the images of concrete structures. The method involves three steps. Firstly, changing the image to grayscale to use the Sobel method to detected edges and find the cracks. Second, determining an appropriate threshold in a binary image and classifying all pixels into two categories: background and foreground, and obtaining the region's area. Finally, using the filter area and changing the area if it is smaller than the specified number. Third, after applying the Sobel filter to eliminate residual noise, performing the Otsu method to detect large cracks. The article describes a method for detecting crack patterns in cement using image processing techniques.