Surface defect sample generation method based on GAN

Fangyi Ni, Xiaojun Wu, Jinghui Zhou, Zhichang Liu
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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.
基于GAN的表面缺陷样本生成方法
为了解决深度学习技术应用于表面缺陷检测任务时训练数据不足的问题,提出了一种基于生成式对抗网络(GAN)的表面缺陷生成算法来增强训练样本数据。首先,设计了一个u型卷积网络,并引入空间自适应归一化结构控制掩模图像生成缺陷形状,完成了从无缺陷图像到缺陷图像的卷积网络;其次,设计多层卷积判别网络,提取真实样本和生成样本的对抗特征;最后,设计了对抗训练损失,完成了生成网络的对抗训练。通过定量对比实验证明,使用表面缺陷生成算法生成数据进行数据增强后,分割网络的分割效果优于不进行数据增强的分割网络。
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