Automated optical inspection method for light-emitting diode defect detection using unsupervised generative adversarial neural network

Che Hsuan Huang, Pei Hsuan Lee, Shu Hsiu Chang, Hao Chung Kuo*, Chia Wei Sun, Chien-Chung Lin, Chun Lin Tsai, Xinke Liu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Many automated optical inspection (AOI) companies use supervised object detection networks to inspect items, a technique which expends tremendous time and energy to mark defectives. Therefore, we propose an AOI system which uses an unsupervised learning network as the base algorithm to simultaneously generate anomaly alerts and reduce labeling costs. This AOI system works by deploying the GANomaly neural network and the supervised network to the manufacturing system. To improve the ability to distinguish anomaly items from normal items in industry and enhance the overall performance of the manufacturing process, the system uses the structural similarity index (SSIM) as part of the loss function as well as the scoring parameters. Thus, the proposed system will achieve the requirements of smart factories in the future (Industry 4.0).

Original languageEnglish
Article number1048
JournalCrystals
Volume11
Issue number9
DOIs
StatePublished - Sep 2021

Keywords

  • Abnormal detection
  • GAN
  • LED
  • Machine learning
  • Unsupervised learning

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