전기학회논문지 (The Transactions of The Korean Institute of Electrical Engineers)

  • 제67권7호
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  • Pages.865-870
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  • 2018
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  • 1975-8359(pISSN)
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  • 2287-4364(eISSN)
대한전기학회 (The Korean Institute of Electrical Engineers)
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금속 표면의 결함 검출을 위한 영역 기반 CNN 기법 비교

Comparison of Region-based CNN Methods for Defects Detection on Metal Surface

  • Lee, Minki (Dept. of Electronics Engineering, Seokyeong University) ;
  • Seo, Kisung (Dept. of Electronics Engineering, Seokyeong University)
  • 투고 : 2018.05.05
  • 심사 : 2018.05.22
  • 발행 : 2018.07.01
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초록

A machine vision based industrial inspection includes defects detection and classification. Fast inspection is a fundamental problem for many applications of real-time vision systems. It requires little computation time and localizing defects robustly with high accuracy. Deep learning technique have been known not to be suitable for real-time applications. Recently a couple of fast region-based CNN algorithms for object detection are introduced, such as Faster R-CNN, and YOLOv2. We apply these methods for an industrial inspection problem. Three CNN based detection algorithms, VOV based CNN, Faster R-CNN, and YOLOv2, are experimented for defect detection on metal surface. The results for inspection time and various performance indices are compared and analysed.

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참고문헌

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