###### Abstract
Thus, in this study [39], a technology for autonomous identification of road cracks is proposed. It is noted that the heterogeneity of the intensity of cracks, and the complexity of the background, complicate the detection. The researchers concluded that an SSD is best suited for this task. In [40], Faster R-CNN and Mask R-CNN are compared and analyzed. The results show that the joint training strategy is very efficient and can ensure that both Faster R-CNN and Mask R-CNN perform the crack detection task with over 90% accuracy when trained with more than 130 images, and it can outperform YOLOv3. However, the collaborative learning strategy causes a decrease in the performance of the bounding box found by Mask R-CNN. In [41], for the detection of cracks in concrete structures, the trained AlexNet model, the prediction accuracy was 99.9%. AlexNet neural networks used a new non-linearity activation function, ReLU, which avoided the gradient disappearing problem. By analyzing the considered architectures of neural networks, it can be noted that the YOLO class operates in a wide range and has high robustness and, when using the SSD network, both large and small objects are determined in one network run; a series of architectures based on R-CNN is not always applicable when detecting objects in real time. Based on the results of the literature review, it can be summarized that AI methods used throughout the entire life cycle of construction projects are increasingly relevant and popular in assessing the properties, defects, and residual life of building products and buildings, as well as their structures [42; 43; 44]. AI is needed where it is necessary to accelerate and stabilize the processes of intellectual activity. YOLO (You Only Look Once) is a neural network architecture designed to detect objects in an image. YOLOv5 \"refers to the One-stage detector architecture--an approach that predicts the coordinates of a certain number of Bounding boxes with the results of classification and the probability of finding an object\" [45]. Accordingly, \"YOLOv5s, YOLOv5m, YOLOv5l and YOLOv5x mean small (s), medium (m), large (l), and very large (x) models. Each model has its pros and cons, but ultimately the differences lie in their complexity, performance, and overall accuracy\" [46]. A lack of research has been found on the identification and monitoring of various defects in facing bricks that occur at various stages of its manufacture. The scientific novelty of the research lies in:  
* application of the developed augmentation algorithm to increase the accumulated database of images of facing bricks [47];
* development of an algorithm that allows the detection of foreign inclusions, broken corners, cracks, and uneven coloring, including the presence of rusty spots on facing bricks;creation, optimization, and testing of the computer vision model based on the CNN YOLOv5s. In this paper, we consider the solution to the problems from the field of building flaw detection using the computer vision method. The main goal was to use the YOLOv5s convolutional neural network to discover and classify various violations of the structure and appearance of finished products of facing bricks that occurred in the production stage during processing, packaging, transportation, or storage. The tasks of this study are as follows:  
* creating a database of images of facing brick samples;
* substantiation and description of the selected YOLOv5s detector;
* carrying out the process of augmentation to expand the training data set;
* implementation, optimization, debugging, and testing of the algorithm using the CNN YOLOv5s;
* determination of the quality of the metrics of the implemented model. The practical value of this study lies in the development of an applied algorithm capable of acting as the basis for intelligent online control of defects in conveyor production products.