Weakly-Supervised Defect Segmentation on Periodic Textures Using CycleGAN

Weakly-Supervised Defect Segmentation on Periodic Textures Using CycleGAN

The importance of an automated defect inspection system has been increasing in the manufacturing industries. Various products to be examined have periodic textures. Among image-based inspection systems, it is common that supervised defect segmentation requires a great number of defect images with th...

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Bibliographic Details
Main Authors: Minsu Kim, Hoon Jo, Moonsoo Ra, Whoi-Yul Kim
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Automated defect inspection
visual inspection system
weakly-supervised learning
periodic textures
data augmentation
generative adversarial networks
Online Access:https://ieeexplore.ieee.org/document/9200489/

Internet

https://ieeexplore.ieee.org/document/9200489/

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