標題: A Texture Generation Approach for Detection of Novel Surface Defects
作者: Lai, Yu-Ting Kevin
Hu, Jwu-Sheng
電機工程學系
Department of Electrical and Computer Engineering
關鍵字: automated optical inspection;surface defect detection;generative adversarial networks
公開日期: 1-一月-2018
摘要: Surface defect detection is challenging due to varying defect types and defect novelties. Because of this, it is hard for algorithms to implement across datasets. Moreover, current automated optical inspection (AOI) machines cannot handle this novelty effectively. In this work, we develop a new method for surface defect detection based on generative models, which can detect novelty according to learned distributions. Experimental results on real industrial datasets show that the proposed method can successfully construct the surface texture pattern generator. By transforming the image through the generator to the corresponding latent space, the defects can be separated effectively without a tedious effort of annotation in a large amount of training data.
URI: http://dx.doi.org/10.1109/SMC.2018.00736
http://hdl.handle.net/11536/151111
ISSN: 1062-922X
DOI: 10.1109/SMC.2018.00736
期刊: 2018 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)
起始頁: 4343
結束頁: 4348
顯示於類別:會議論文