CycleGAN により得られた画像を用いた塗装不良の検出

Translated title of the contribution: Detection of Painting Defects using Background Subtraction with CycleGAN

Sota Sugiyama, Naoyuki Aikawa

Research output: Contribution to journalArticlepeer-review

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 contributionDetection of Painting Defects using Background Subtraction with CycleGAN
Original languageJapanese
Pages (from-to)80-81
Number of pages2
JournalIEEJ Transactions on Electronics, Information and Systems
Volume144
Issue number2
DOIs
Publication statusPublished - Feb 2024

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