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

TL;DR

Defect-GAN is a novel generative model that creates realistic defect samples to enhance the training of defect inspection neural networks, addressing data scarcity in manufacturing defect detection.

Contribution

This paper introduces Defect-GAN, a defect synthesis network with a unique architecture that produces diverse, realistic defects and improves defect inspection accuracy.

Findings

Synthesizes diverse, high-fidelity defect images

Enhances defect inspection network performance

Outperforms existing defect generation methods

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.