Abstract
In recent years, defect detection and classification using machine learning as an alternative to visual inspection has been studied. In this paper, we propose a method for defect detection by taking the difference between pseudo-images generated using CycleGAN and the original images. Compared to the conventional detection method using binarization, our proposed method can detect defects independent of the shooting environment, thus significantly reducing the risk of overlooking defects.
Translated title of the contribution | Detection of Painting Defects using Background Subtraction with CycleGAN |
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Original language | Japanese |
Pages (from-to) | 80-81 |
Number of pages | 2 |
Journal | IEEJ Transactions on Electronics, Information and Systems |
Volume | 144 |
Issue number | 2 |
DOIs | |
Publication status | Published - Feb 2024 |