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DC Field | Value | Language |
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dc.contributor.author | Tian, L | - |
dc.contributor.author | Wang, Z | - |
dc.contributor.author | Liu, W | - |
dc.contributor.author | Cheng, Y | - |
dc.contributor.author | Alsaadi, FE | - |
dc.contributor.author | Liu, X | - |
dc.date.accessioned | 2021-11-08T11:53:32Z | - |
dc.date.available | 2021-11-08T11:53:32Z | - |
dc.date.issued | 2021-09-04 | - |
dc.identifier.citation | Tian, L., Wang, Z., Liu, W., Cheng, Y., Alsaadi, F.E. and Liu, X. (2021) 'A New GAN-Based Approach to Data Augmentation and Image Segmentation for Crack Detection in Thermal Imaging Tests', Cognitive Computation, 13 (5), pp. 1263-1273. doi: 10.1007/s12559-021-09922-w. | en_US |
dc.identifier.issn | 1866-9956 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/23451 | - |
dc.description.abstract | © The Author(s) 2021. As a popular nondestructive testing (NDT) technique, thermal imaging test demonstrates competitive performance in crack detection, especially for detecting subsurface cracks. In thermal imaging test, the temperature of the crack area is higher than that of the non-crack area during the NDT process. By extracting the features of the thermal image sequences, the temperature curve of each spatial point is employed for crack detection. Nevertheless, the quality of thermal images is influenced by the noises due to the complex thermal environment in NDT. In this paper, a modified generative adversarial network (GAN) is employed to improve the image segmentation performance. To improve the feature extraction ability and alleviate the influence of noises, a penalty term is put forward in the loss function of the conventional GAN. A data preprocessing method is developed where the principle component analysis algorithm is adopted for feature extraction. The data argumentation technique is utilized to guarantee the quantity of the training samples. To validate its effectiveness in thermal imaging NDT, the modified GAN is applied to detect the cracks on the eddy current pulsed thermography NDT dataset. | en_US |
dc.description.sponsorship | Institutional Fund Projects under grant no. (IFPIP-220-135-1442); Ministry of Education and King Abdulaziz University, Jeddah, Saudi Arabia; National Natural Science Foundation of China under Grants 61873148, 61933007 and 61903065; China Postdoctoral Science Foundation under Grant 2018M643441; Royal Society of the UK; Alexander von Humboldt Foundation of Germany. | en_US |
dc.format.extent | 1263 - 1273 | - |
dc.format.medium | Print-Electronic | - |
dc.language.iso | en_US | en_US |
dc.publisher | Springer Nature | - |
dc.rights | © The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/. | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | generative adversarial network | en_US |
dc.subject | thermal imaging test | en_US |
dc.subject | nondestructive testing | en_US |
dc.subject | crack detection | en_US |
dc.subject | principal component analysis | en_US |
dc.title | A New GAN-Based Approach to Data Augmentation and Image Segmentation for Crack Detection in Thermal Imaging Tests | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1007/s12559-021-09922-w | - |
dc.relation.isPartOf | Cognitive Computation | - |
pubs.issue | 5 | - |
pubs.publication-status | Published | - |
pubs.volume | 13 | - |
dc.identifier.eissn | 1866-9964 | - |
Appears in Collections: | Dept of Computer Science Research Papers |
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