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.
|Number of pages||12|
|Journal||International Journal of Precision Engineering and Manufacturing|
|State||Published - 1 Apr 2020|
Bibliographical noteFunding Information:
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (2017R1A2B4004231) and by the MOTIE [Ministry of Trade, Industry and Energy (10079560)] and Development of materials and core-technology for future display support program. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
© 2020, Korean Society for Precision Engineering.
- Deep learning
- Defect inspection
- Machine vision
- Object detection