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Cigarette Defect Detection using Convolutional Neural Network

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  • URLhttps://db.koreascholar.com/Article/Detail/425692
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한국기계기술학회지 (Journal of the Korean Society of Mechanical Technology)
한국기계기술학회 (Korean Society of Mechanical Technology)
초록

In factory automation, efforts are being made to increase productivity while maintaining high-quality products. In this study, a CNN network structure was designed to quickly and accurately recognize a cigarette located in the opposite direction or a cigarette with a loose end in an automated facility rotating at high speed for cigarette production. Tobacco inspection requires a simple network structure and fast processing time and performance. The proposed network has an excellent accuracy of 96.33% and a short processing time of 0.527 msec, showing excellent performance in learning time and performance compared to other CNN networks, confirming its practicality. In addition, it was confirmed that efficient learning is possible by increasing a small number of image data through a rotation conversion method.

저자
  • 박희문(경상국립대학교) | Hee-Mun Park
  • 김민(경상국립대학교 메카트로닉스공학과) | 김민
  • 전향식(한국항공우주연구원) | JUN HYANG SIG
  • 황광복(경상국립대학교) | Hwang Kwang Bok
  • 박진현(경상국립대학교) | 박진현