Journal
INTERNATIONAL JOURNAL OF PRECISION ENGINEERING AND MANUFACTURING
Volume 21, Issue 4, Pages 747-758Publisher
KOREAN SOC PRECISION ENG
DOI: 10.1007/s12541-019-00269-9
Keywords
Defect inspection; Machine vision; Deep learning; Object detection
Funding
- National Research Foundation of Korea (NRF) - Ministry of Science, ICT and Future Planning [2017R1A2B4004231]
- MOTIE [Ministry of Trade, Industry and Energy] [10079560]
- Korea Evaluation Institute of Industrial Technology (KEIT) [10079560] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
- National Research Foundation of Korea [2017R1A2B4004231] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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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.
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