Classification for Painting Defects Using Two-Step Deep Learning Models

被引：0

作者：

Adachi K. ^{[1
]}

Natori T. ^{[1
]}

Aikawa N. ^{[1
]}

机构：

[1] Faculty of Advanced Engineering, Tokyo University of Science, 6-3-1, Niijuku, Katsushika-ku, Tokyo

来源：

IEEJ Transactions on Electronics, Information and Systems | 2022年 / 142卷 / 11期

关键词：

deep learning; image processing; painting defects; two-step classification; visual inspection;

D O I：

10.1541/ieejeiss.142.1243

中图分类号：

学科分类号：

摘要：

Recently, visual inspection methods using deep learning have been proposed. In this paper, we propose a classification method using two-step deep learning. The first step is to determine whether the painting is a defect or not, and the second step is to determine the kind of painting defect. By comparing the results using various deep learning models, we show that the classification accuracy is higher than that of conventional method. © 2022 The Institute of Electrical Engineers of Japan.

引用

页码：1243 / 1244

页数：1