Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/23310
Title: An improved generative adversarial network with modified loss function for crack detection in electromagnetic nondestructive testing
Authors: Tian, L
Wang, Z
Liu, W
Cheng, Y
Alsaadi, FE
Liu, X
Keywords: Crack detection;Generative adversarial networks;Image segmentation;Image processing;Electromagnetic nondestructive testing
Issue Date: 29-Jul-2021
Publisher: Springer
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
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.
URI: http://bura.brunel.ac.uk/handle/2438/23310
DOI: http://dx.doi.org/10.1007/s40747-021-00477-9
ISSN: 2199-4536
2198-6053
Appears in Collections:Dept of Computer Science Research Papers

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