https://scholars.lib.ntu.edu.tw/handle/123456789/606995
標題: | Automated optical inspection method for light-emitting diode defect detection using unsupervised generative adversarial neural network | 作者: | Huang C.-H Lee P.-H Chang S.-H Kuo H.-C Sun C.-W Lin C.-C Tsai C.-L Liu X. CHIEN-CHUNG LIN |
關鍵字: | Abnormal detection;GAN;LED;Machine learning;Unsupervised learning | 公開日期: | 2021 | 卷: | 11 | 期: | 9 | 來源出版物: | Crystals | 摘要: | 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). ? 2021 by the authors. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114749736&doi=10.3390%2fcryst11091048&partnerID=40&md5=65e855feef3ad21c8ce46efe4ebbe915 https://scholars.lib.ntu.edu.tw/handle/123456789/606995 |
ISSN: | 20734352 | DOI: | 10.3390/cryst11091048 |
顯示於: | 電機工程學系 |
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