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.
Advancement in information and communication technology has led to tremendous development in graphics techniques. Evolving multimedia tools are used to generate high quality Computer Graphics (CG) images. These images have wide applications in domains like video gaming, augmented reality, and virtual reality and many other. Computer graphic images are also used illegally in criminal activities. This article proposes an effective transfer learning approach to classify CG and Photographic (PG) images available in small scale dataset. Initially, pre-trained models such as AlexNet, GoogleNet, ResNet50, VGG-18 and SqueezeNet were modified and fine-tuned appropriately. Based on the validation accuracy, SqueezeNet was adapted as learning model for extracting deep features for classification. To evaluate the performance of squeezeNet, Columbia dataset and Photo realistic dataset were used. Finally, the performance of the proposed model was compared with state-of-the- art transfer learning approaches to prove its efficacy. Accuracy of 93.75% was attained using SqueezeNet for the folding ratio 80:20 when the input data is augmented.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.