A Surface Defect Detection Method Based on Positive Samples

Zhao et al. combined GAN, autoencoder (AE) and LBP to detect defects on texture surface, needing only positive samples without any defect sample nor manual label.

“Notably, Zhao et al. [113] combined GAN and autoencoder (AE) and LBP to detect defects on texture surface, which needs only positive samples without any defect sample nor manual label.”

The GAN-AE-LBP framework is of better practical application value due to its unsupervised nature.

“This framework is of better practical application value due to its unsupervised natures.”

DCGAN builds constraints on traditional GANs to overcome unstable output and often works in unsupervised learning manner for defect detection.

“The deep convolutional generative adversarial network (DCGAN) [111] is a kind of CNN, which build certain constraints on traditional generative adversarial networks (GANs) to overcome its drawback of unstable output, and it often works in unsupervised learning manner for defect detection [112,113].”

GAN-based image data augmentation algorithms are mainly used for relatively few samples datasets with high-resolution, but they are not applied to few samples datasets images with low-resolution on an industrial scale.

“These kinds of algorithms are mainly used for relatively few samples datasets with high-resolution, but they are not applied to few samples datasets images with low-resolution on an industrial scale [44].”