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

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.

Automated summary

An improved GAN is proposed for crack detection in electromagnetic NDT, with additional regulation terms introduced to enhance the contrast ratio of the generated image. Experimental results demonstrate the superiority of the improved GAN over the original one in crack detection tasks.