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High-resolution Classifier Ensemble for Defect Inspection of Display Panels
Eunwoo Kim(김은우),Jaewon Kim(김재완) 제어로봇시스템학회 2021 제어·로봇·시스템학회 논문지 Vol.27 No.8
DL(Deep Learning) has been applied to various tasks, and image classification is one of the most active areas where DL is applied. While the majority of DL approaches have focused on achieving improvements in classification accuracy by innovating a network structure with standard datasets in low resolution, detailed features in a high-resolution dataset are crucial to enhance classification accuracy, especially for practical datasets in the industry. We proposed a DL classifier structure that fully utilizes high-definition information for an OLED (Organic Light-Emitting Diode) panel inspection, and, for verification, we performed the task of classifying the authenticity of the actual OLED panel defects. The authenticity inspection of panel defects requires a precise analysis of high-resolution information. We confirmed that the application of the proposed classifier structure improved the performance of the classification. The proposed method includes an object detection method optimized for panel inspection and displays stable performance by utilizing an ensemble structure. The proposed method has been applied and is being used in actual production lines.