# Detecting Cracks in Aerated Concrete Samples Using a Convolutional Neural Network
###### Abstract
To prevent the premature destruction of products and structures, various methods for the early detection of defects on construction objects based on the algorithm [30] have already been studied and put into practice, using convolutional neural networks on images obtained via laser fusion in a powder layer [31], based on deep learning [32; 33], using a generative adversarial network [34]. In [2; 35; 36; 37; 38; 39; 40], the authors investigated the possibility of using deep learning methods to detect defects in buildings and structures, as well as various building materials. Thus, in [2], the possibility of using a convolutional neural network and transfer learning to detect cracks in stone walls was studied. Based on the results of the research, a neural network was developed that allows the identification of cracks on the surface of brickwork with an accuracy of up to 95.3%. Moreover, in [38], the authors considered the possibility of unmanned aerial vehicles (UAVs) equipped with vision sensors to search for cracks in various structures of bridge structures. Based on 384 crack photographs, a convolutional neural network was developed, and the results of field tests proved the effectiveness of using this network and UAVs to find and quantify cracks in structures. The authors [39] also studied the possibility of identifying cracks in concrete structures using UAV technology and hybrid image processing. This scheme turned out to be effective for finding and measuring cracks with an opening width of more than 0.1 mm, and the maximum error was 7.3%. In [40], the possibility of using a convolutional neural network to automatically search for various defects in buildings and structures from images was evaluated. According to the results of the study, the reliability and ability to accurately localize the defect was proven. In the works [41; 42; 43; 44], the effectiveness of the application of deep learning methods in the engineering industry was studied. In [41], a probabilistic neural network algorithm was developed for detecting defects in the welding process. Weld defect texture images were considered as input parameters for building the model, and in terms of the output, the developed FAST-PNN model provides fairly accurate data on detected defects (burns, pores, cracks, and so on). Thus, in studies [42; 43], the authors developed neural networks that make it possible to find defects in welds with an accuracy of more than 93%. In [44], a 2D convolutional neural network was developed to control the quality of 3D ball joints, which allows one to find defective products with an accuracy of 97%. An interesting study is [45], where using deep learning methods, a smartphone application was developed to monitor the performance of buildings in real time. The resulting model has high precision. However, this model can be improved by using large datasets. In [46], a model was developed for the real-time monitoring of concrete structures, consisting of the Efficient-NetB0 backbone network and detectors. The model was trained on a data set with concrete surface defects, and the test results showed a high precision of defect detection--74.6% for cracks and 89.9% for open bars. The analysis showed the growing relevance and popularity of using artificial intelligence methods in the construction industry at all stages of construction, from determining the mechanical properties of materials to expert systems that provide recommendations for assessing the reliability of building structures of buildings and structures on various grounds. Particular attention is paid to AI methods specifically when searching for defects in building structures and materials. This is justified by the desire of builders to exclude the influence of the human factor, as well as to reduce time and labor costs. In this paper, we consider the use of the YOLOv4 convolutional neural network for crack detection on building material samples. The YOLOv4 model has optimal speed (due to the fact that the model is a single-stage detector) and high object-detection precision.