Conditional Generative Adversarial Network for Defect Classification with Class Imbalance

Abstract

Automated Optical Inspection (AOI) is used for defect inspection during industrial manufacturing process. It uses optical instrument to snap the surface of products and identify defects through technique of machine vision processing. Deep learning and convolution neural network automatically produce the feature which are useful for identify the defect correctly. However, the class imbalance for number of defect samples and normal samples is typically in industrial process, which will lead to poor accuracy of deep learning model. This paper integrate pix2pix, a Conditional Generative Adversarial Network (GAN), which can generate synthetic image automatically, to generate more defect images to adjust the data distribution for class imbalance. Eventually, this paper uses Dense Convolutional Network (DenseNet) to get better result of defect data classification with manipulated data than with original data.