Domain Adaptation for Automatic OLED Panel Defect Detection Using Adaptive Support Vector Data Description

Abstract

Detection of surface defects on organic light emitting diode (OLED) panels pose challenges such as irregular shapes and sizes along with varying textures and patterns on the panels. These challenges can be addressed by designing invariant features and training an anomaly detection algorithm such as support vector data description (SVDD). However, these hand designed features may not be capable of handling test datasets that have undergone distributional shift due to changes in lighting configuration or panel specification. This leads to a degradation of the classifier performance. In this paper, we propose a domain adaptation technique for outlier detection called as adaptive support vector data description (A-SVDD) to tackle distributional change in OLED panel datasets. The proposed method aims to learn an incremental classifier based on the existing classifier using an objective function similar to SVDD. We also investigate the application of features called as local inlier---outlier ratios augmented with modified local binary pattern (LBP) for detection of OLED panel defects in the context of SVDD and A-SVDD. In the experiments, the proposed domain adaptation technique is compared with baseline methods and existing approaches to demonstrate its effectiveness. A detailed evaluation of the features was performed in the context of A-SVDD and SVDD on several defects like scratch, spot, stain and pit to demonstrate that the combination of local inlier---outlier ratios and modified LBP significantly increases the detection accuracy.