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ORIGINAL RESEARCH article
Front. Phys.
Sec. Optics and Photonics
Volume 12 - 2024 |
doi: 10.3389/fphy.2024.1458194
Non-destructive testing based on Unet-CBAM network for pulsed thermography
Provisionally accepted- Ningbo University, Ningbo, China
Infrared thermography (IRT) is a non-destructive testing technique that can detect the internal defects of materials. In the detection of austenitic stainless-steel pipes with large curvature, image noise caused by uneven heating is difficult to avoid. Traditional image processing methods are less effective. According to previous works, a supervised neural network was proposed in this paper using Unet network and convolutional block attention module. Existing image processing method and networks were used to compare with the proposed method. The results show that the proposed method can remove the noise caused by uneven heating, and detect all subsurface defects in stainless-steel pipe.
Keywords: infrared thermography, image processing, Neural Network, Finite Element Analysis, pipeline
Received: 02 Jul 2024; Accepted: 09 Sep 2024.
Copyright: © 2024 Wu, Wu and Zhu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Dan Wu, Ningbo University, Ningbo, China
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