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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 | 2021-10-07T10:25:28Z | - |
dc.date.available | 2021-07-29 | - |
dc.date.available | 2021-10-07T10:25:28Z | - |
dc.date.issued | 2021-07-29 | - |
dc.identifier.citation | Tian, L., Wang, Z., Liu, W. et al. An improved generative adversarial network with modified loss function for crack detection in electromagnetic nondestructive testing. Complex Intell. Syst. (2021). https://doi.org/10.1007/s40747-021-00477-9 | en_US |
dc.identifier.issn | 2199-4536 | - |
dc.identifier.issn | 2198-6053 | - |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/23310 | - |
dc.description.abstract | In this paper, an improved generative adversarial network (GAN) is proposed for the crack detection problem in electromagnetic nondestructive testing (NDT). To enhance the contrast ratio of the generated image, two additional regulation terms are introduced in the loss function of the underlying GAN. By applying an appropriate threshold to the segmentation of the generated image, the real crack areas and the fake crack areas (which are affected by the noises) are accurately distinguished. Experiments are carried out to show the superiority of the improved GAN over the original one on crack detection tasks, where a real-world NDT dataset is exploited that consists of magnetic optical images obtained using the electromagnetic NDT technique. | en_US |
dc.description.sponsorship | Institutional Fund Projects; Ministry of Education and King Abdulaziz University, Jeddah, Saudi Arabia; National Natural Science Foundation of China; China Postdoctoral Science Foundation; Royal Society; Alexander von Humboldt Foundation | en_US |
dc.format.extent | 1 - 10 (10) | - |
dc.language | English | - |
dc.language.iso | en | en_US |
dc.publisher | Springer | en_US |
dc.subject | Crack detection | en_US |
dc.subject | Generative adversarial networks | en_US |
dc.subject | Image segmentation | en_US |
dc.subject | Image processing | en_US |
dc.subject | Electromagnetic nondestructive testing | en_US |
dc.title | An improved generative adversarial network with modified loss function for crack detection in electromagnetic nondestructive testing | en_US |
dc.type | Article | en_US |
dc.identifier.doi | http://dx.doi.org/10.1007/s40747-021-00477-9 | - |
dc.relation.isPartOf | Complex & Intelligent Systems | - |
pubs.publication-status | Published online | - |
pubs.volume | 2021 | - |
dc.identifier.eissn | 2198-6053 | - |
Appears in Collections: | Dept of Computer Science Research Papers |
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FullText.pdf | 3.33 MB | Adobe PDF | View/Open |
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