병렬 구조의 다중 필터 CNN을 이용한 골절합용 판의 불량 탐지 모델에 관한 연구 (A Study on Defect Detection Model of Bone Plates Using Multiple Filter CNN of Parallel Structure)

서지정보

· 발행기관 : 한국정밀공학회
· 수록지 정보 : 한국정밀공학회지 / 40권 / 9호 / 677 ~ 683페이지
· 저자명 : 이송연, 허용정

초록

Bone plates are a medical device used for fixing broken bones, which should not have a crack and hole defect. Defect detection is very important because bone plate defect is very dangerous. In this study, we proposed a defect detection model based on a parallel type convolution neural network for detecting bone plate crack and pore deformation. All size filters were different according to the defect shape. A convolution neural network detected pore defects. Another convolution neural network detected the crack. Two convolution neural networks simultaneously detected different defect types. The performance of the defect detection model was measured and used for the F1-score. We confirmed that performance of the defect detection model was 98.4%. We confirmed that the defect detection time was 0.21 seconds.

영어초록

Bone plates are a medical device used for fixing broken bones, which should not have a crack and hole defect. Defect detection is very important because bone plate defect is very dangerous. In this study, we proposed a defect detection model based on a parallel type convolution neural network for detecting bone plate crack and pore deformation. All size filters were different according to the defect shape. A convolution neural network detected pore defects. Another convolution neural network detected the crack. Two convolution neural networks simultaneously detected different defect types. The performance of the defect detection model was measured and used for the F1-score. We confirmed that performance of the defect detection model was 98.4%. We confirmed that the defect detection time was 0.21 seconds.

참고자료

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