모아레 현상을 이용한 평면부재 변형탐지와 기계학습을 활용한 인식방법
Detection of Plane Member Deformation using Moire Fringe and Recognition using Machine Learning
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초록
본 연구는 모아레 현상을 이용하여 구조물의 평면부재의 변형을 탐지하는 방식을 개발하였다. 평행선형상인 기존연구와 비교하여 본 연구의 원형형상은 변형 시 고유한 무늬가 나타나 변형의 방향도 시각적으로 인지할 수 있었다. 또한 변형을 확실하고 자동으로 구분할 수 있도록 기계학습의 이미지분류를 사용하여 탐지한 결과 이미지 표면이 오염되더라도 KNN, SVM방식으로는 95%의 인식률, AlexNet과 Gogglenet Inception으로는 99%의 인식률을 보여 실 현장활용이 가능할 것으로 판단된다.
Abstract
This study presents a method for detecting deformation of a structure using moire fringe. Compared with the previous study result obtained by using a parallel line shape, the circular shape showed a unique pattern at the time of deformation and the direction of deformation could be visually recognized. In addition, image classification using machine learning was applied so that deformations were clearly and automatically distinguished. Even if the image surface was contaminated, the recognition rate was 95% in KNN and SVM, and was 99% in AlexNet and GoggleNet inception, which implies that field application is possible.
참고문헌 (14)
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