# Detection and Evaluation of Construction Cracks through Image Analysis Using Computer Vision
## 1 Introduction
### State of the Art
The proposed neural network was evaluated on the training and test information, obtaining 96.32% and 94.29% accuracy, respectively. On the other hand, ref. [3] studied the efficiency of using a deep neural network (YOLOv5) to identify concrete fractures, specifically in bridges, using drone images. The authors focused on obtaining high precision (mAP = 0.976) without sacrificing identification speed using focal loss, pruning, and data scaling techniques. Regarding the use of thermal imaging cameras in the construction industry, ref. [14] developed the BIA (Building Insulation Analyzer) software to measure the differences between the interior and exterior temperatures at three points on the facade of a building, whether openings such as window corners or doors. Two images were taken with a thermal camera and one with a reflex camera to evaluate temperatures. A variation of 2 \({}^{\circ}\)C was obtained in the openings and 5 \({}^{\circ}\)C in the insulated and thick walls. A method based on the analysis of thermal images that consists of three phases is proposed by [15]: collecting original and thermal images, estimating the position of the captured image, and updating the 3D model of the construction site in the BIM software. The proposal was applied to a building under construction in Suzhou, China. In conclusion, when processing the original images, it failed to identify the elements of the building due to low image quality, poor light conditions, and image noise. In the case of the mixture of the original and thermal images, a high percentage of object detection (95%) was obtained. On the other hand, ref. [16] evaluated the application of machine learning and image analysis algorithms to analyze metallic structures from thermographic images. It was found that minor defects can be detected when they are closer to the surface. Therefore, it was suggested to use higher resolution images to obtain more pixels per fault image. Regarding the characterization of fractures, there is extensive literature on the subject. Still, we can highlight the seminal work of [17], who sought to provide a quantitative description of the fracture surface in polymer-based concrete and concluded that the geometry of a surface fracture was dependent on the scale of observation. More recent works, such as that of [18], sought to understand and characterize the evolution of a concrete fracture process using the Digital Image Correlation technique; they concluded that the size of the fracture area or zone in a piece of concrete is related to the size of the constructive element. On the other hand, ref. [19] used an X-ray scanner to generate computed tomography images to assess the evolution of damage in the internal structure of the concrete under stress situations. Finally, ref. [20] unified the techniques of fractal dimensions and neural networks (YOLOv5, among others) to segment the fractures. However, although the computational complexity of the chosen method was relatively low, they were unable to measure or estimate the size of the fracture. In this line of research, the development of mechanisms for the automation of the control and monitoring of construction processes is proposed based on the use of artificial intelligence and the capture of images from conventional cameras (PTZ and Bullet) and infrared cameras. The aim is to develop an alert and prediction model that automatically and constantly monitors the construction process during its execution, with the ability to detect deficiencies or failures (cracks) in the concrete.