인공지지체 불량 분류를 위한 기계 학습 알고리즘 성능 비교에 관한 연구

A Study on Performance Comparison of Machine Learning Algorithm for Scaffold Defect Classification

  • 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 : 2020.09.01
  • 심사 : 2020.09.22
  • 발행 : 2020.09.30

초록

In this paper, we create scaffold defect classification models using machine learning based data. We extract the characteristic from collected scaffold external images using USB camera. SVM, KNN, MLP algorithm of machine learning was using extracted features. Classification models of three type learned using train dataset. We created scaffold defect classification models using test dataset. We quantified the performance of defect classification models. We have confirmed that the SVM accuracy is 95%. So the best performance model is using SVM.

키워드

참고문헌

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