금속 표면의 결함 검출을 위한 영역 기반 CNN 기법 비교

다국어 초록 (Multilingual Abstract)

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.

목차 (Table of Contents)

• Abstract
• 1. 서론
• 2. 결함 검사 구분 및 위치검출
• 3. CNN 기반 결함 검출 기법
• 4. 실험 및 결과 분석
• 5. 결론
• References

참고문헌 (Reference)

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