Słoński Marek, Schabowicz Krzysztof, Krawczyk Ewa
Faculty of Civil Engineering, Cracow University of Technology, Warszawska 24, 31-155 Kraków, Poland.
Faculty of Civil Engineering, Wrocław University of Science and Technology, Wybrzeże Wyspiańskiego 27, 50-370 Wrocław, Poland.
Materials (Basel). 2020 Mar 27;13(7):1557. doi: 10.3390/ma13071557.
Non-destructive testing of concrete for defects detection, using acoustic techniques, is currently performed mainly by human inspection of recorded images. The images consist of the inside of the examined elements obtained from testing devices such as the ultrasonic tomograph. However, such an automatic inspection is time-consuming, expensive, and prone to errors. To address some of these problems, this paper aims to evaluate a convolutional neural network (CNN) toward an automated detection of flaws in concrete elements using ultrasonic tomography. There are two main stages in the proposed methodology. In the first stage, an image of the inside of the examined structure is obtained and recorded by performing ultrasonic tomography-based testing. In the second stage, a convolutional neural network model is used for automatic detection of defects and flaws in the recorded image. In this work, a large and pre-trained CNN is used. It was fine-tuned on a small set of images collected during laboratory tests. Lastly, the prepared model was applied for detecting flaws. The obtained model has proven to be able to accurately detect defects in examined concrete elements. The presented approach for automatic detection of flaws is being developed with the potential to not only detect defects of one type but also to classify various types of defects in concrete elements.
目前，使用声学技术对混凝土进行缺陷检测的无损检测主要通过人工检查记录的图像来完成。这些图像由从超声层析成像仪等检测设备获取的被检测构件内部组成。然而，这种自动检测既耗时又昂贵，而且容易出错。为了解决其中一些问题，本文旨在评估卷积神经网络（CNN）在利用超声层析成像对混凝土构件中的缺陷进行自动检测方面的性能。所提出的方法有两个主要阶段。在第一阶段，通过基于超声层析成像的检测获取并记录被检测结构内部的图像。在第二阶段，使用卷积神经网络模型对记录的图像中的缺陷进行自动检测。在这项工作中，使用了一个大型的预训练CNN。它在实验室测试期间收集的一小部分图像上进行了微调。最后，将准备好的模型应用于缺陷检测。所获得的模型已被证明能够准确检测被检测混凝土构件中的缺陷。所提出的缺陷自动检测方法不仅有潜力检测一种类型的缺陷，还能够对混凝土构件中的各种类型缺陷进行分类。
