A more reliable defect detection and performance improvement method for panel inspection based on artificial intelligence
Eui-Young Jeong, Jaewon Kim, Won-Hyouk Jang, Hyun‐Chang Lim, Hanaul Noh, Jong-Myong Choi
Abstract
This paper presents a practical approach to automatic inspection of display panels based on deep neural networks. The approach accurately detects appearance defects on display panels in various sizes and shapes within a short computation time. We propose a novel reliable detection network using the multi-channel parameter reduction method, which preserves high-resolution features of defects at sub-sampling steps of convolutional operations. Our proposed network consists of two sub-networks with different functions: pixel-wise segmentation of defect regions and distinction of real defects from fake defects. Compared with conventional deep learning networks, the proposed network achieved a more accurate detection rate, i.e. an F1-score of 81%, for real defect images acquired from an actual display manufacturing process. In addition, we propose a conditionally paired generative network that generates synthetic images of scarce defects under four different lighting conditions. The proposed networks improved the detection accuracy and can be applied to automatic inspection processes in display manufacturing factories in place of human inspection.