Defect-GAN: High-Fidelity Defect Synthesis for Automated Defect Inspection

Abstract

Automated defect inspection is critical for effective and efficient maintenance, repair, and operations in advanced manufacturing. On the other hand, automated defect inspection is often constrained by the lack of defect samples, especially when we adopt deep neural networks for this task. This paper presents Defect-GAN, an automated defect synthesis network that generates realistic and diverse defect samples for training accurate and robust defect inspection networks. Defect-GAN learns through defacement and restoration processes, where the defacement generates defects on normal surface images while the restoration removes defects to generate normal images. It employs a novel compositional layer-based architecture for generating realistic defects within various image backgrounds with different textures and appearances. It can also mimic the stochastic variations of defects and offer flexible control over the locations and categories of the generated defects within the image background. Extensive experiments show that Defect-GAN is capable of synthesizing various defects with superior diversity and fidelity. In addition, the synthesized defect samples demonstrate their effectiveness in training better defect inspection networks.

arXiv commentCodes will not be released due to confidentiality agreement. Published on WACV 2021. (https://openaccess.thecvf.com/content/WACV2021/papers/Zhang_Defect-GAN_High-Fidelity_Defect_Synthesis_for_Automated_Defect_Inspection_WACV_2021_paper.pdf)

References in corpus (10)

• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• Prototypical Networks for Few-shot Learning
• GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
• Large Scale GAN Training for High Fidelity Natural Image Synthesis
• Data Augmentation Generative Adversarial Networks
• Unsupervised Attention-guided Image to Image Translation
• LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation
• Differentiable Augmentation for Data-Efficient GAN Training
• Dynamic Few-Shot Visual Learning without Forgetting