Vision Based Crack Detection in Concrete Structures Using Cutting-Edge Deep Learning Techniques

Abstract

Concrete crack detection is the process of inspecting the concrete structures. If the defects present in any structures could not be detected in time, it may have a severe impact. The cracks can be detected using destructive as well as non-destructive testing (NDT) techniques. This article presents image based NDT techniques for detecting the concrete cracks using the cutting edge deep learning techniques. NDT is the process of analysing the materials, components, structures etc. without causing any damage to it. In this paper, a transfer learning technique is proposed for detecting the cracks in the concrete structures. A dataset of 40000 images of concrete which is collected from METU Campus is analysed in NVIDIA Tesla V100 12 GB GPU servers using various recent deep learning techniques and the results are tabulated. Performance of four pre trained network architectures such as Alexnet, VGG16, VGG19 and ResNet-50 is assessed for categorizing the images. From the results, it is revealed that the residual neural network technique is successful in detecting the cracks with high accuracy and less complexity.