Environmentally-Robust Defect Classification With Domain Augmentation Framework
Author:
Affiliation:
1. Department of Data Science, Seoul National University of Science and Technology, Nowon-gu, Seoul, Republic of Korea
Funder
Korea Institute for Advancement of Technology (KIAT) through Korean Government [Ministry of Trade, Industry and Energy (MOTIE)]
National Research Foundation of Korea (NRF) through Korean Government
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx8/6287639/10380310/10663395.pdf?arnumber=10663395
Reference40 articles.
1. Machine Learning for industrial applications: A comprehensive literature review
2. Machine Vision Based Traffic Sign Detection Methods: Review, Analyses and Perspectives
3. Deep CNN-based visual defect detection: Survey of current literature
4. Machine learning-based imaging system for surface defect inspection
5. An Overview of Deeply Optimized Convolutional Neural Networks and Research in Surface Defect Classification of Workpieces
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