Unsupervised anomaly detection of MEMS in low illumination based on polarimetric Support Vector Data Description

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Affiliation of Author(s):Tianjin University

Journal:Optics Express

Place of Publication:USA

Funded by:National Key Research and Development Program of China

Key Words:无监督缺陷检测，偏振增强，

Abstract:Lowilluminated images make it challenging to conduct anomaly detection on materialsurface. Adding polarimetric information helps expand pixel range and recover background structure of network inputs. In this letter, an anomaly detection method in low illumination is proposed which utilizes polarization imaging and patch-wise Support Vector Data Description (SVDD) model. Polarimetric information of Micro Electromechanical System (MEMS) surface is captured by a division-of-focal- plane (DoFP) polarization camera and used to enhance low illuminated images. The enhanced images without defects ser

All the Authors:Yaokang Huang, Mei Sang*, Lun Xing, Haofeng Hu, and Tiegen Liu

First Author:Yaokang Huang

Indexed by:Applied Research

Correspondence Author:Mei Sang

Volume:29

Issue:22

Page Number:35651-35663

Translation or Not:no

CN No.:null