###### Abstract
Additionally, the use of computer vision in detecting defects in railway tracks has already been studied [16; 17]. Machine learning methods have been successfully applied in order to detect and monitor surface defects of photovoltaic panels (PV) [18; 19]. The improved MobilenetV1-YOLOv4 network has been successfully used to detect insulation defects and thereby improve the security of power lines [20]. The work [21] describes the use of a model for monitoring the state of a conveyor belt structure using machine learning and connecting to the Internet of things, as well as an ultra-high-frequency RFID sensor [21]. The YOLO-RFF method, based on an extended field of sensations and feature fusion, has proven itself in the accurate and high-speed detection of various industrial defects [22]. The ResNeXt + YOLO v3, Inception v3 + YOLO v3, and YOLO v3 models, which have an integrated structure, were studied in order to categorize and locate defects in ceramic substrates to reduce the human resource costs associated with manual re-inspection [23]. Machine learning methods have shown their high accuracy in detecting and monitoring defects in buildings (multiscale feature fusion method: 3 ScaleNetwork deep convolutional neural network, local binary pattern, simple linear iterative clustering) [24], retaining walls (machine vision combined with drones) [25], bridges (improved YOLOv3 network, combining high- and low-resolution element images) [26], and pavements (model BV-YOLOv5S (BiFPN Varifocal Loss-YOLOv5S)) [27]. There are quite a variety of methods for detecting and monitoring defects on the surface and inside concrete [28; 29; 30; 31]. Compared to manual instrumentation methods such as ultrasonic [31], non-destructive non-contact inspection methods using thermal imaging and AI are preferable to control the depth of cracks in concrete [29; 30]. Of great importance for this study was the already available information on the research and application of various methods for detecting, measuring, and monitoring defects in brick and brick masonry structures [32; 33; 34; 35; 36; 37]. A sensor technology based on smart bricks--piezoresistive brick sensors for a structural health monitoring system--has been used to monitor deformations and early detection of cracks in shear brickwork [32]. Non-destructive electro-acoustic methods (resonant pulse method or ultrasonic pulse method) were applied in order to discover and analyze defects in the internal structure of bricks. The most successful model of the resonant pulse method had a success rate of up to 85% [33]. Using a break of ordinary quartz optical fibers (6-12 pieces) attached to the surface of the masonry with epoxy adhesive to detect and monitor structural cracks in brickwork, with a detection probability tending to one, allowed us to determine the residual life of structural elements of a building exposed to natural and man-made anomalous phenomena [34]. Automatic brick segmentation and image crack quantification using machine learning was implemented using various AI methods. The accuracy of checking the brick detection model was 96.86% [35]. The DeepLabV3 model for efficient automatic crack segmentation in masonry and actual measurement of crack length in masonry has outperformed previous state-of-the-art crack segmentation models according to the authors of [37]. The Convolutional Neural Network (CNN) model showed better results in detecting cracks in brickwork images taken from the Internet (81.0% accuracy) than in laboratory images (61.5%). The features were built from grayscale image fragments that were focused on dark areas indicating the appearance of cracks [36]. The work [38] presents an improved SSD (Single Shot MultiBox Detector) algorithm, which is used to detect cracks in the workpiece. The algorithm can effectively detect small cracks; the image detection accuracy is over 80%. In recent years, systems for recognizing pavement cracks based on computer vision have become widespread.