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.

在本文中，针对电磁无损检测 (NDT) 中的裂纹检测问题，提出了一种改进的生成对抗网络 (GAN)。为了提高生成图像的对比度，在底层 GAN 的损失函数中引入了两个额外的调节项。通过对生成图像的分割应用适当的阈值，可以准确地区分真实裂纹区域和假裂纹区域（受噪声影响）。进行了实验以证明改进的 GAN 在裂纹检测任务上优于原始 GAN，其中利用真实世界的 NDT 数据集，该数据集由使用电磁 NDT 技术获得的磁光学图像组成。