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Pattern deep region learning for crack detection in thermography diagnosis system

Lookup NU author(s): Dr Bin Gao, Professor Gui Yun TianORCiD, Ying Yin

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Abstract

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. Eddy Current Pulsed Thermography is a crucial non-destructive testing technology which has a rapidly increasing range of applications for crack detection on metals. Although the unsupervised learning method has been widely adopted in thermal sequences processing, the research on supervised learning in crack detection remains unexplored. In this paper, we propose an end-to-end pattern, deep region learning structure to achieve precise crack detection and localization. The proposed structure integrates both time and spatial pattern mining for crack information with a deep region convolution neural network. Experiments on both artificial and natural cracks have shown attractive performance and verified the efficacy of the proposed structure.

Publication metadata

Author(s): Hu J, Xu W, Gao B, Tian GY, Wang Y, Wu Y, Yin Y, Chen J

Publication type: Article

Publication status: Published

Journal: Metals

Year: 2018

Volume: 8

Issue: 8

Online publication date: 06/08/2018

Acceptance date: 01/08/2018

Date deposited: 29/08/2018

ISSN (electronic): 2075-4701

Publisher: MDPI AG

URL: https://doi.org/10.3390/met8080612

DOI: 10.3390/met8080612

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