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- W4285252376 abstract "Defect detection in thin-film-transistor liquid-crystal displays (TFT-LCDs) is crucial for ensuring the quality of the display. However, because of the diversity of TFT-LCD panel defects, accurate localization and detection become difficult. To overcome these problems, this study used deep learning and image processing algorithms that automatically detect TFT array defects and presents a laser-cutting path that removes only array defect regions. First, the YOLOv4 model was used to locate the defect and glass region of interest (ROI) in panel images, and a semantic segmentation model (FCN-VGG16) was used to identify defect and glass pixel positions in the ROI. Finally, the effective cutting range of faults was determined using overlap and nonoverlap cutting (ON-cutting) judgment methods. In this investigation, three types of defects were used: D1, D2, and D3. According to the proposed ON-cutting methodology, the error repair rates of D1-, D2-, and D3-type defects were 4.79%, 0%, and 0%, respectively. Therefore, these strategies can help manufacturers enhance the quality of TFT-LCD panels by identifying and locating the optimal glass cutting line and defective pixels." @default.
- W4285252376 created "2022-07-14" @default.
- W4285252376 creator A5071391610 @default.
- W4285252376 creator A5072379956 @default.
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- W4285252376 date "2022-01-01" @default.
- W4285252376 modified "2023-10-18" @default.
- W4285252376 title "Deep Learning and Image Processing Techniques for Recognizing Liquid-Crystal Display Array Residue and the Automatic Planning of Laser-Cutting Segments" @default.
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- W4285252376 doi "https://doi.org/10.1109/tim.2022.3188550" @default.
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