2021
DOI: 10.1002/jsid.997
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Liquid crystal display defects in multiple backgrounds with visual real‐time detection

Abstract: There are kinds of defects that may appear in the process of Liquid Crystal Display (LCD) manufacturing, which cannot be effectively detected, owing to the uneven illumination, low contrast, and miscellaneous patterns of defects. To improve the efficiency of defect detection and ensure the quality of LCD, three visual real‐time detection methods are adopted for detecting six different defects in multiple backgrounds, where image preprocessing methods are used to highlight the defects and facilitate the segment… Show more

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Cited by 11 publications
(8 citation statements)
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References 22 publications
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“…Fan et al [ 23 ] used polynomial fitting for background reconstruction and threshold segmentation to obtain defect candidate regions, which could efficiently detect low-contrast defects. Cui et al [ 24 ] adopted the Otsu’s method to select defect candidate regions, then used variance and meshing to detect Mura and edge defects. In addition to the above methods, there are defect detection methods based on defect features, such as color feature [ 25 ], similarity of histogram [ 26 ], and the dictionary learning method [ 27 ].…”
Section: Related Workmentioning
confidence: 99%
“…Fan et al [ 23 ] used polynomial fitting for background reconstruction and threshold segmentation to obtain defect candidate regions, which could efficiently detect low-contrast defects. Cui et al [ 24 ] adopted the Otsu’s method to select defect candidate regions, then used variance and meshing to detect Mura and edge defects. In addition to the above methods, there are defect detection methods based on defect features, such as color feature [ 25 ], similarity of histogram [ 26 ], and the dictionary learning method [ 27 ].…”
Section: Related Workmentioning
confidence: 99%
“…The automated detection and classification technology for display screen defects has attracted considerable scholarly attention as a result of the difficulty of detecting them due to factors such as low contrast, uneven color, and complex background. [2][3][4][5][6][7] For improving the accuracy of defect detection, some research works concentrated on enhancing the defect features. For example, the image grayscale curve method can be applied to solve the problems of low contrast and inconspicuous edges of Mura defects.…”
Section: Related Workmentioning
confidence: 99%
“…The automated detection and classification technology for display screen defects has attracted considerable scholarly attention as a result of the difficulty of detecting them due to factors such as low contrast, uneven color, and complex background 2‐7 …”
Section: Introductionmentioning
confidence: 99%
“…Additionally, difficulties arise when the input image contains numerous linear edge features, making challenging to automatically filter LCD screen's edge using LSD or Hough Transform methods. Currently, DL methods demonstrated remarkable success in realm of feature extraction, target finding, target recognition research [10]. Here, suggests employing DL method forlocalization of LCD screens, the detection of defects in smart meters.…”
Section: Introductionmentioning
confidence: 99%