Ambiguous Surface Defect Image Classification of AMOLED Displays in Smartphones

In this paper, we propose a classification approach for ambiguously shaped defects found on the surface of a type of display panel module that is widely used in the field of mobile displays. These types of surface defects are difficult to properly distinguish due to defect similarity and diversity. In such cases, defect types can only be determined using cumbersome human visual inspection. To solve the problem of ambiguous surface defect classification, we introduce a novel filtering method that effectively separates the foreground defective regions from the background, which has structured patterns, local illumination variation, and different light conditions for each of several cameras in an inspection system. Applying the proposed filter method to defect images, we select important features by adopting a wrapper-based feature selection method using a random forest as a learning algorithm. Successful classification results using the presented model are obtained using challenging real-world defect image data gathered from a smart phone display module inspection line in an industrial plant.