Surface defect sample generation method based on GAN

Abstract

In order to solve the insufficiency of training data when deep learning technology is applied to surface defect detection task, a surface defect generation algorithm based on generative adversarial network (GAN) was proposed to enhance training sample data. First, a U-shaped convolutional network was designed, and a spatial adaptive normalized structure was introduced to control the mask image to generate the defect shape, and the network from defect-free image to defect image was completed. Second, a multi-layer convolutional discriminant network is designed to extract adversarial feature of the real samples and generated samples. Finally, the adversarial training loss was designed and the generative network adversarial training was completed. Through quantitative contrast experiment, it is proved that the segmentation network has better segmentation results than without data augmentation after using the surface defect generation algorithm to generate data for data augmentation.