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.
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