The importance of ultrasonic nondestructive testing has been increasing in recent years, and there are high expectations for the potential of laser ultrasonic visualization testing, which combines laser ultrasonic testing with scattered wave visualization technology. Even if scattered waves are visualized, inspectors still need to carefully inspect the images. To automate this, this paper proposes a deep neural network for automatic defect detection and localization in LUVT images. To explore the structure of a neural network suitable to this task, we compared the LUVT image analysis problem with the generic object detection problem. Numerical experiments using real-world data from a SUS304 flat plate showed that the proposed method is more effective than the general object detection model in terms of prediction performance. We also show that the computational time required for prediction is faster than that of the general object detection model.

Recently, we have witnessed the rapid development of artificial intelligence technology, of which deep learning is a representative example. It is used in a wide range of fields, and its performance has become known. Particularly, the development of supervised learning, such as deep learning, has progressed at a breathtaking pace, and it is also expected to be utilized in the civil engineering field. However, its progress there has lacked the steady development seen in other fields. In this study, we aim to indicate areas that should be addressed in the field of civil engineering in the future. Moreover, using the field of infrastructure maintenance and management as an example, and with a particular focus on data, this study presents the outlook for data platforms in terms of data consolidation and storage, measurement automation for data collection, and ways of linking data and knowledge. This paper is the English translation from the authors’ previous work [Chun, P. J. (2020). "A.I. in civil engineering: a roadmap for research and development." Artificial Intelligence and Data Science, 1(J1), 9-15. (in Japanese)].

Infrared methods that can remotely detect internal damage by capturing thermal images often miss damaged areas when the judgment is made by humans. Additionally, although there have been moves to introduce autodiscovery through convolutional neural networks as part of infrared technology, such methods have not had a sufficient level of precision due to a lack of supervised training data. Hence, in this study, we focus on self-supervised learning. In self-supervised learning, even if there is a lack of supervised labels, it is still possible to realize a high degree of accuracy. Moreover, we present an example of introducing self-supervised learning via the infrared method and validate the effectiveness of the same. This paper is the English translation from the authors’ previous work [Kawanowa, S. et. al., (2022). "Automatic detection of inner defects of concrete by analyz-ing thermal images using self-supervised learning." Artificial Intelligence and Data Science, 3(J2), 47-55. (in Japanese)].