Deep Learning Based Defect Inspection Using the Intersection Over Minimum Between Search and Abnormal Regions

Eunjeong Choi, Jeongtae Kim

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)747-758
Number of pages12
JournalInternational Journal of Precision Engineering and Manufacturing
Volume21
Issue number4
DOIs
StatePublished - 1 Apr 2020

Keywords

  • Deep learning
  • Defect inspection
  • Machine vision
  • Object detection

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