본문 바로가기
HOME> 논문 > 논문 검색상세

논문 상세정보

Procedia CIRP v.72, 2018년, pp.1069 - 1072  

A New Ensemble Approach based on Deep Convolutional Neural Networks for Steel Surface Defect classification

Chen, Wen (State Key Laboratory of Digital Manufacturing Equipment & Technology, Huazhong University of Scienceand Technology, Wuhan, 430074, China ) ; Gao, Yiping (State Key Laboratory of Digital Manufacturing Equipment & Technology, Huazhong University of Scienceand Technology, Wuhan, 430074, China ) ; Gao, Liang (State Key Laboratory of Digital Manufacturing Equipment & Technology, Huazhong University of Scienceand Technology, Wuhan, 430074, China ) ; Li, Xinyu (State Key Laboratory of Digital Manufacturing Equipment & Technology, Huazhong University of Scienceand Technology, Wuhan, 430074, China ) ;
  • 초록  

    Abstract Steel surface defect recognition is a crucial component of automated steel surface inspection system which influences the quality of steel greatly. To improve the accuracy rate, an ensemble approach that integrating different deep convolutional neural networks (DCNNs) is proposed in this paper. Firstly, three different DCNNs are trained respectively with data augmentation to reduce over-fitting. Various optimization methods and tricks are used to reduce the error in the training procedure. Secondly, three well-trained models are combined. The experimental results show that the proposed approach made a state-of-art performance on accuracy rate and robustness in steel surface defect classification.


  • 주제어

    Deep Convoltional Neural Network .   Ensemble Learning .   Steel Surface Defect Recognition .   Data Augmentation.  

 활용도 분석

  • 상세보기

    amChart 영역
  • 원문보기

    amChart 영역

원문보기

유료 다운로드의 경우 해당 사이트의 정책에 따라 신규 회원가입, 로그인, 유료 구매 등이 필요할 수 있습니다. 해당 사이트에서 발생하는 귀하의 모든 정보활동은 NDSL의 서비스 정책과 무관합니다.

원문복사신청을 하시면, 일부 해외 인쇄학술지의 경우 외국학술지지원센터(FRIC)에서
무료 원문복사 서비스를 제공합니다.

NDSL에서는 해당 원문을 복사서비스하고 있습니다. 위의 원문복사신청 또는 장바구니 담기를 통하여 원문복사서비스 이용이 가능합니다.

이 논문과 함께 출판된 논문 + 더보기