Defect Enhancement Generative Adversarial Network for Enlarging Data Set of Microcrack Defect

This paper presents a micro defect data set expansion method focuses on the microcrack defect of magnetic ring. Deep neural networks require a mass of training samples to be fully optimized. However, it is difficult to obtain a mass of defective samples in industrial field. In the case of insufficient samples, using GANs (Generative Adversarial Networks) for data expansion can effectively solve the problems of model over-fitting and low detection accuracy caused by insufficient training samples. However, it is difficult for conventional GANs to generate microcrack defective samples of high quality. This paper presents Defect Enhancement Generative Adversarial Network (DEGAN). This model can generate microcrack defects with obvious defect characteristics and high diversity. The experimental results show that the defective samples generated by DEGAN are very close to the real ones. The data set amplified by this model can significantly optimize deep neural network and achieve higher defect detection accuracy.