An improved generative adversarial network with modified loss function for crack detection in electromagnetic nondestructive testing

• Published in: Complex & intelligent systems Vol. 8; no. 1; pp. 467 - 476
• Main Authors: Tian, Lulu, Wang, Zidong, Liu, Weibo, Cheng, Yuhua, Alsaadi, Fuad E., Liu, Xiaohui
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.02.2022; Springer Nature B.V
• Subjects: Complexity; Computational Intelligence; Data Structures and Information Theory; Engineering; Generative adversarial networks; Image contrast; Image enhancement; Image segmentation; Nondestructive testing; Original Article; Image segmentation; Generative adversarial networks; Crack detection; Image processing; Electromagnetic nondestructive testing
• ISSN: 2199-4536; 2198-6053
• DOI: 10.1007/s40747-021-00477-9

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.