标题: | 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. 资讯工程学系 电控工程研究所 Department of Computer Science Institute of Electrical and Control Engineering |
公开日期: | 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) |
起始页: | 1444 |
结束页: | 1449 |
显示于类别: | Conferences Paper |