Design and evaluation of features and classifiers for OLED panel defect recognition in machine vision

With the rapid growth of organic light-emitting diode (OLED) display devices, the industrial manufacturing of OLED panels is currently an expanding global reality. Regarding quality control, automatic defect detection and classification are undoubtedly indispensable. Although defect detection systems have been widely considered in the literature, classification systems have not received appropriate attention. This study proposes the design of an efficient and high-performance system for defect classification by combining well-known machine-learning algorithms: support vector machine, random forest (RF), and k-nearest neighbours. To begin, possible features are designed and feature selection using principal component analysis and RF is investigated to automatically select the most effective features. Then, a hierarchical structure of classifiers is proposed for efficiently adjusting the rates of true defect and fake defect classification. The proposed system is evaluated over a database of 3502 images captured from real OLED display devices in different illumination conditions. The defects in the database are divided into 10 classes corresponding to the types of true defect and fake defect. The experiments confirm that the proposed system can achieve an accuracy of up to 94.0% for the binary classification of true defect and fake defect and an overall recognition rate of 86.3% for the 10 sub-classes.