Deep Learning Based Defect Inspection Using the Intersection Over Minimum Between Search and Abnormal Regions
- 주제(키워드) Deep learning , Defect inspection , Machine vision , Object detection
- 등재 SCIE, SCOPUS, KCI등재
- 발행기관 SpringerOpen
- 발행년도 2020
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000165892
- 본문언어 영어
- Published As http://dx.doi.org/10.1007/s12541-019-00269-9
- 저작권 이화여자대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
We present a deep learning based defect inspection system that detects bounding boxes for any identified defect regions. In contrast to existing deep learning based object detection methods, the proposed method detects defects based on the intersection over minimum between a proposal region and defect regions rather than the well-known intersection over union, since intersection over minimum is more effective to detect variously sized defects. The proposed method also provides significant improvements over existing methods such as efficient training by minimizing cross entropy loss function, and efficient defect detection using multiple proposal boxes for the defect and entire image. We verified that the proposed method provides improved performance compared with existing methods using simulation and experimental studies. © 2020, Korean Society for Precision Engineering.
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