標題: Industrial Anomaly Detection and One-class Classification using Generative Adversarial Networks
作者: Lai, Y. T. K.
Hu, J. S.
Tsai, Y. H.
Chiu, W. Y.
資訊工程學系
電控工程研究所
Department of Computer Science
Institute of Electrical and Control Engineering
公開日期: 1-一月-2018
摘要: Industrial image datasets for quality inspection are mostly sparse in defects. It is then hard for both automated optical inspection (AOI) machines and simple neural network classifiers to inspect all defects effectively. In this work, we develop a novel framework for industrial anomaly detection in one-class classification manner, which utilized pre-trained generative adversarial networks (GANs) as the rule of thumb to perform anomaly detection. Our results show that GANs are able to capture arbitrary and structural industrial images and can effectively discern defects when the query images are defective.
URI: http://hdl.handle.net/11536/150777
ISSN: 2159-6255
期刊: 2018 IEEE/ASME INTERNATIONAL CONFERENCE ON ADVANCED INTELLIGENT MECHATRONICS (AIM)
起始頁: 1444
結束頁: 1449
顯示於類別:會議論文