반도체디스플레이기술학회지 (Journal of the Semiconductor & Display Technology)

한국반도체디스플레이기술학회 (The Korean Society Of Semiconductor & Display Technology)
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인공지지체 불량 검출을 위한 딥러닝 모델 성능 비교에 관한 연구

A Comparative Study on Deep Learning Models for Scaffold Defect Detection

  • 이송연 (한국기술교육대학교대학원 메카트로닉스공학과) ;
  • 허용정 (한국기술교육대학교 메카트로닉스공학부)
  • Lee, Song-Yeon (Mechatronics Engineering, Graduate School of Korea University of Technology and Education) ;
  • Huh, Yong Jeong (Department of Mechatronics Engineering, Korea University of Technology and Education)
  • 투고 : 2021.06.10
  • 심사 : 2021.06.22
  • 발행 : 2021.06.30
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초록

When we inspect scaffold defect using sight, inspecting performance is decrease and inspecting time is increase. We need for automatically scaffold defect detection method to increase detection accuracy and reduce detection times. In this paper. We produced scaffold defect classification models using densenet, alexnet, vggnet algorithms based on CNN. We photographed scaffold using multi dimension camera. We learned scaffold defect classification model using photographed scaffold images. We evaluated the scaffold defect classification accuracy of each models. As result of evaluation, the defect classification performance using densenet algorithm was at 99.1%. The defect classification performance using VGGnet algorithm was at 98.3%. The defect classification performance using Alexnet algorithm was at 96.8%. We were able to quantitatively compare defect classification performance of three type algorithms based on CNN.

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참고문헌

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  2. Song-Yeon Lee and Yong-Jeong Huh, "A Study on Prediction Model of Scaffold Pore Size Using Machine Learning", J. of The Korean Society of Semiconductor & Display Technology, Vol.18, pp. 46-50, 2019.
  3. Song-Yeon Lee and Yong-Jeong Huh, "A Study on Square Pore Shape Discrimination Model of Scaffold Using Machine Learning Based Multiple Linear Regression", J. of The Korean Society of Semiconductor & Display Technology, Vol.19, pp. 59-64, 2020.
  4. Song-Yeon Lee and Yong-Jeong Huh, "A Study on Shape Warpage Defect Detection Model of Scaffold Using Deep Learning Based CNN", J. of The Korean Society of Semiconductor & Display Technology, Vol.19, pp. 59-64, 2021.
  5. Chang-Hee Yang, Kyu-Sub Park, Young-Sub Kim and Yong-Hwan Lee, "Comparative Analysis for Emotion Expression Using Three Methods Based by CNN", J. of The Korea Society of Semiconductor & Display Technology, Vol.19, pp.65-70, 2020.
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