|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.
Department of Computer Science
Institute of Electrical and Control Engineering
|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.|
|期刊:||2018 IEEE/ASME INTERNATIONAL CONFERENCE ON ADVANCED INTELLIGENT MECHATRONICS (AIM)|
|Appears in Collections:||Conferences Paper|