Mura defect detection method based on sample learning and human visual characteristics

Abstract

The invention discloses a mura defect detection method based on sample learning and human visual characteristics, which belongs to the TFT-LCD display defect detection field. According to the invention, the method comprises the following steps: firstly, utilizing the Gaussian filter smoothing and Hough transform rectangle to preprocess the TFT-LCD display image, removing a large amount of noise and segmenting the image areas to be detected; then, using the PCA algorithm to conduct learning to a large amount of defect-free samples; automatically extracting the differential characteristics between the background and the target and re-constructing a background image; and then, thresholding the differential characteristics between a testing image and the background; through the reconstructing of the background and the threshold calculating, jointly creating a model. According to the invention, based on the training sample learning, a relationship model between the background structure information and the threshold value is established; and a self-adaptive segmentation algorithm based on human visual characteristics is proposed. The main purpose of the invention is to detect different mura defects in a TFT-LCD, to raise the qualification rate and to increase accuracy for the detection of mura defects.

Description

technical field [0001] The invention belongs to the technical field of TFT-LCD display defect detection, and in particular relates to a mura defect detection method based on sample learning and human visual characteristics. Background technique [0002] The mura defect on a TFT-LCD liquid crystal display is a typical low-contrast target. Mura comes from Japanese and is used to describe the brightness imbalance perceived by people when viewing a display. Visually, mura defects generally appear as low-contrast areas with no fixed shape and blurred edges that can be perceived by the human eye. Along with the rapid development of microelectronics technology, liquid crystal displays are developing in the direction of large screen, low power consumption, light and thin, and high resolution. Such a trend will greatly increase the probability of display defects while bringing many advantages such as high visual effect and portability. At present, most of the detection of mura def...

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to overcome the conventional manual detection of mura defects on TFT-LCD liquid crystal displays in the prior art, the detection cost is relatively high, the detection time is long, the detection accuracy and production line production efficiency are low, and the existing mura Due to the relatively low detection accuracy and detection efficiency of the automatic defect detection method, a mura defect detection method based on sample learning and human visual characteristics is provided

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