파라미터에 따른 인공지지체 불량 탐지 모델의 성능 비교

Performance Comparison of Scaffold Defect Detection Model by Parameters

  • 이송연 (한국기술교육대학교 대학원 메카트로닉스공학과) ;
  • 허용정 (한국기술교육대학교 메카트로닉스공학부)
  • Song Yeon Lee (Department of Mechatronics Engineering, Graduate School of Korea University of Technology and Education) ;
  • Yong Jeong Huh (School of Mechatronics Engineering, Korea University of Technology and Education)
  • 투고 : 2023.03.02
  • 심사 : 2023.03.22
  • 발행 : 2023.03.31

초록

In this study, we compared the detection accuracy of the parameters of the scaffold failure detection model. A detection algorithm based on convolutional neural network was used to construct a failure detection model for scaffold. The parameter properties of the model were changed and the results were quantitatively verified. The detection accuracy of the model for each parameter was compared and the parameter with the highest accuracy was identified. We found that the activation function has a significant impact on the detection accuracy, which is 98% for softmax.

키워드

참고문헌

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