A Study on Image Inspection Method for Foreign Object Contamination of Cut Vegetables based on Generative Adversarial Network

Abstract

Currently, cut vegetables are visually inspected for foreign matter, and a highly reliable automatic foreign matter inspection method is required to improve productivity and quality. However, vegetables contain pattern variations such as leaf veins and coloration, and it is not easy to learn normal products compared to standard industrial parts inspection methods that have small variations in normal products. In this study, considering the properties of foreign matter, we proposed a foreign matter image inspection method using a generative adversarial network that can learn the variation of normal vegetables, and verified its effectiveness.