Detecting Cracks in Aerated Concrete Samples Using a Convolutional Neural Network

Abstract

The creation and training of artificial neural networks with a given accuracy makes it possible to identify patterns and hidden relationships between physical and technological parameters in the production of unique building materials, predict mechanical properties, and solve the problem of detecting, classifying, and segmenting existing defects. The detection of defects of various kinds on elements of building materials at the primary stages of production can improve the quality of construction and identify the cause of particular damage. The technology for detecting cracks in building material samples is of great importance in building monitoring, in pre-venting the spread of defective material. In this paper, we consider the use of the YOLOv4 convolutional neural network for crack detection on building material samples. This was based on the creation of its own empirical database of images of samples of aerated concrete. The number of images was increased by applying our own augmentation algorithm. Optimization of the parameters of the intellectual model based on the YOLOv4 convolutional neural network was performed. Experimental results show that the YOLOv4 model developed in this article has high precision in defect detection problems: AP@50 = 85% and AP@75 = 68%. It should be noted that the model was trained on its own set of data obtained by simulating various shooting conditions, rotation angles, object deformations, and light distortions through image processing methods, which made it possible to apply the developed algorithm in practice.

Detection and Dispersion Analysis of Water Globules in Oil Samples Using Artificial Intelligence Algorithms

“…“ AP@ 50 and AP@ 75 values were calculated showing AP values calculated at IoU = 0.50 and IoU = 0.75 respectively” [ 53 , 54 ]; in this study, AP@ 50 = 89% and AP@ 75 = 78%. If the level of accuracy obtained using the indicated metrics is lower than the level of accuracy declared by the researcher (in practice, from 85%), then it is necessary to increase the number of images in the training set and/or add additional effects to the augmentation process and retrain the CNN.…”

“…Precision and recall can be calculated using the formulas: where “true positive ( TP ) is the correct detection made by the model; false positive ( FP ) is incorrect detection made by the detector; false negative ( FN ) is a true result missed (not detected) by the detector” [ 53 , 54 ].…”

“…Geometric operations: image rotation/reflection, random image shifts along the Ox and Oy axes, and image rotation by 90°, 180°, and 270° [ 53 , 54 ].…”

Analysis of Geometric Characteristics of Cracks and Delamination in Aerated Concrete Products Using Convolutional Neural Networks

“…[36], the authors developed and tested convolutional neural networks to search for cracks on a concrete road using unmanned aerial vehicles. Research on monitoring and finding various types of defects using computer vision is also presented in [37][38][39][40][41][42], covering methods for detecting cracks in concrete based on images [37]; detecting defects in brick and cellular concrete samples to prevent the spread of defective products [38,40]; and detecting and segmenting cracks on the surface of concrete [41].…”

“…This type of cellular concrete is quite easy to use and process, has good soundproofing and thermal insulation characteristics, fire safety and environmental friendliness. Laying aerated concrete blocks is a simple and fairly quick process, and the roughness of the surface of the products makes them easier to process [39].…”