Journal of the Korean Society of Visualization (한국가시화정보학회지)

The Korean Society of Visualization (한국가시화정보학회)
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A rubber o-ring defect detection system using data augmentation based on the SinGAN and random forest algorithm

SinGAN기반 데이터 증강과 random forest알고리즘을 이용한 고무 오링 결함 검출 시스템

  • Received : 2021.10.04
  • Accepted : 2021.11.29
  • Published : 2021.12.31
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Abstract

In this study, data was augmentation through the SinGAN algorithm using small image data, and defects in rubber O-rings were detected using the random forest algorithm. Unlike the commonly used data augmentation image rotation method to solve the data imbalance problem, the data imbalance problem was solved by using the SinGAN algorithm. A study was conducted to distinguish between normal products and defective products of rubber o-ring by using the random forest algorithm. A total of 20,000 image date were divided into transit and testing datasets, and an accuracy result was obtained to distinguish 97.43% defects as a result of the test.

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Acknowledgement

이 성과는 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임(No. 2020R1A5A8018822).

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