Automated Optical Inspection Method for Light-Emitting Diode Defect Detection Using Unsupervised Generative Adversarial Neural Network

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 simultaneousl...

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Bibliographic Details
Published in:Crystals (Basel) 2021-09, Vol.11 (9), p.1048
Main Authors: Huang, Che-Hsuan, Lee, Pei-Hsuan, Chang, Shu-Hsiu, Kuo, Hao-Chung, Sun, Chia-Wei, Lin, Chien-Chung, Tsai, Chun-Lin, Liu, Xinke
Format: Article
Language:English
Subjects:
abnormal detection
Algorithms
Datasets
Defects
Factories
GAN
Inspection
LED
Light emitting diodes
Machine learning
Manufacturing
Neural networks
Object recognition
unsupervised learning
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Summary: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).
ISSN:2073-4352
2073-4352
DOI:10.3390/cryst11091048