基于生成对抗网络数据扩充的缺陷识别方法

可视化无损检测（NDT）在深度学习技术发展下，在数据处理方面正面领着巨大的机遇。但是，获取足够的标记数据集是一个很大的挑战。实现无损检测图像数据集的扩充有利于提升深度学习在缺陷检测中的能力。因此，通过研究无损检测图像数据特点，结合循环一致生成对抗网络（CycleGANs）方法，对现有的数据进行了有效的扩充。改善了深度卷积神经元网络（DCNN）从而有效的利用扩充数据来提升对缺陷图像的识别能力。最后，通过对比实验，展示了本扩充数据对提升缺陷检测网络训练具有重要作用。

GANs based synthetic data augmentation for defects recognition

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.