병렬형 합성곱 신경망을 이용한 골절합용 판의 탐지 성능 비교에 관한 연구

A Study on Detection Performance Comparison of Bone Plates Using Parallel Convolution Neural Networks

  • 이송연 (한국기술교육대학교 대학원 메카트로닉스공학과) ;
  • 허용정 (한국기술교육대학교 메카트로닉스공학부)
  • Lee, Song Yeon (Department of Mechatronics Engineering, Graduate School of Korea University of Technology and Education) ;
  • Huh, Yong Jeong (School of Mechatronics Engineering, Korea University of Technology and Education)
  • 투고 : 2022.09.02
  • 심사 : 2022.09.21
  • 발행 : 2022.09.30

초록

In this study, we produced defect detection models using parallel convolution neural networks. If convolution neural networks are constructed parallel type, the model's detection accuracy will increase and detection time will decrease. We produced parallel-type defect detection models using 4 types of convolutional algorithms. The performance of models was evaluated using evaluation indicators. The model's performance is detection accuracy and detection time. We compared the performance of each parallel model. The detection accuracy of the model using AlexNet is 97 % and the detection time is 0.3 seconds. We confirmed that when AlexNet algorithm is constructed parallel type, the model has the highest performance.

키워드

과제정보

이 논문은 2022년도 한국기술교육대학교 교수 교육연구진흥과제 지원에 의하여 연구되었음.

참고문헌

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