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Machine Learning-based Automatic Optical Inspection System with Multimodal Optical Image Fusion Network

Abstract

This paper proposes an automatic cast product surface defect detection system based on deep learning artificial intelligence technology. Application of deep learning is difficult because of the uneven surface and small defects of the cast product which are easily affected by the lighting position and angle. Therefore, three channel fusion data from an optical system that simultaneously acquires a 2D surface image and 3D shape information of the target object were obtained and used for deep learning. The mean average precision (mAP) of the proposed defect detection model using the three-channel fusion data is about 77%. And this result is greater than the 60% mAP of a defect detection model that uses single-channel data. For further optimization, we investigate a deep learning model that employs a deep learning network with multiple models, where each model trains and detects only a single type of defect. The experimental results demonstrate that the mAP of the model was improved to 88%.

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Correspondence to Min Young Kim.

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This study has been conducted with the BK21 FOUR project funded by the Ministry of Education, Korea (4199990113966) and Basic Science Research program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2021R1A6A1A03043144). This work was partially supported by Korea Institute for Advancement of Technology (KIAT) grant funded by the Korea Government (MOTIE)(N0002428, The Competency Development Program for Industry Specialist) and by the Korea Institute of Industrial Technology as “Research equipment coordination project (kitech JJ-16-0001)”.

Jong Hyuk Lee received his B.S. degree from the Catholic University of Daegu, Daegu, Korea in 2017, and an M.S. degree from the Kyungpook National University, Daegu, Korea. He is currently pursuing a Ph.D. degree at the Kyungpook National University, His research interests include data engineering, deep learning, and inspection systems.

Byeong Hak Kim received his Ph.D. from the School of Electronic Engineering at Kyungpook National University, Daegu, Korea. He was a Senior Engineer at the SAMSUNG THALES and HANWHA Systems, Korea. He is currently a Senior Researcher at the Korea Institute of Industrial Technology. His research interests include IR and 3D imaging systems, visual object tracking, ML/DL object detection, 3D laser radar, and counter drone systems.

Min Young Kim received his B.S., M.S., and Ph.D. degrees from the Korea Advanced Institute of Science and Technology, Korea in 1996, 1998, and 2004, respectively. He worked as a Senior Researcher for Mirae Corp. from 2004 to 2005 and as a Chief Research Engineer for Kohyoung Corp. from 2005 to 2009 in the field of artificial vision systems for intelligent machines and robots. In 2009, he joined the School fo Electrical Engineering and Computer Science of the Kyungpook National University as an Assistant Professor. He is currently a Professor in the School of Electronics Engineering at the same university and is the Deputy Director of the KNU-LG Convergence Research Center and Director of the Research Center for Neurosurgical Robotic Systems. He was a visiting Associate Professor in the Department of Electrical and Computer Engineering and School of Medicine at Johns Hopkins University from 2014 to 2015. His research interest interests include visual intelligence for robotic perception and recognition of autonomous unmanned ground and aerial vehicles.

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Lee, J.H., Kim, B.H. & Kim, M.Y. Machine Learning-based Automatic Optical Inspection System with Multimodal Optical Image Fusion Network. Int. J. Control Autom. Syst. 19, 3503–3510 (2021). https://doi.org/10.1007/s12555-020-0118-1

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Keywords

  • Deep learning
  • defect detection
  • machine vision
  • optical system