Visualized nondestructive testing (NDT), with the development of deep learning technology, is leading a huge opportunity in data processing. However, obtaining sufficient labeled data sets is a big challenge. The realization of the expansion of the nondestructive inspection image data set is conducive to improving the ability of deep learning in defect detection. Therefore, this article has effectively expanded the existing data by studying the characteristics of nondestructive testing image data, combined with CycleGANs (CycleGANs) method. Improved the deep convolutional neural network (DCNN) to effectively use the expanded data to improve the ability to recognize defective images. Finally, through comparative experiments, it is shown that this expanded data has an important role in improving the training of the defect detection network.