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

Low illuminated images make it challenging to conduct anomaly detection on material surface. 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 serve as training sets of model to make it available for anomaly detection. The proposed method can generate heatmaps to locate defects correctly. It reaches 0.996 anomaly scores, which is 22.4% higher than that of low illuminated images and even higher than normal illuminated images.