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http://dx.doi.org/10.6109/jkiice.2022.26.6.834

Comparative analysis of Machine-Learning Based Models for Metal Surface Defect Detection  

Lee, Se-Hun (Department of Artificial Intelligence, Kyungpook National University)
Kang, Seong-Hwan (Department of Artificial Intelligence, Kyungpook National University)
Shin, Yo-Seob (Department of Artificial Intelligence, Kyungpook National University)
Choi, Oh-Kyu (Artificial Intelligence Research Center, Industry Applications Research Division, Korea Electrotechnology Research Institute (KERI))
Kim, Sijong (Artificial Intelligence Research Center, Industry Applications Research Division, Korea Electrotechnology Research Institute (KERI))
Kang, Jae-Mo (Department of Artificial Intelligence, Kyungpook National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.26, no.6, 2022 , pp. 834-841 More about this Journal
Abstract
Recently, applying artificial intelligence technologies in various fields of production has drawn an upsurge of research interest due to the increase for smart factory and artificial intelligence technologies. A great deal of effort is being made to introduce artificial intelligence algorithms into the defect detection task. Particularly, detection of defects on the surface of metal has a higher level of research interest compared to other materials (wood, plastics, fibers, etc.). In this paper, we compare and analyze the speed and performance of defect classification by combining machine learning techniques (Support Vector Machine, Softmax Regression, Decision Tree) with dimensionality reduction algorithms (Principal Component Analysis, AutoEncoders) and two convolutional neural networks (proposed method, ResNet). To validate and compare the performance and speed of the algorithms, we have adopted two datasets ((i) public dataset, (ii) actual dataset), and on the basis of the results, the most efficient algorithm is determined.
Keywords
Metal Surface; Defect Detection; Machine Learning; Convolutional Neural Network;
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