Research Of The Mura Defect Detection Of TFT-LCD Based On Computer Vision

With the rapid development of LCD technology, a new type of flat panel displaynamed TFT-LCD is becoming the preference of display terminal gradually. TFT-LCDis now getting more and more ubiquitous due to its unique characteristics such as lowpower consumption, ultra-thin, high brightness and contrast, rapid response and so on.Meanwhile, to meet the market’s demand, the development trends of TFT-LCD will beoriented to high-resolution, thin, light, low power consumption and large screen. Duringits manufacturing process, the size of the glass substrate materials and the associatedcomponents will be expanded gradually. However, its thickness will be reduced, whichleads to a rapid increase of the probability which Mura defects occur. Most LCDmanufacturers still apply the traditional human visual inspection method to Muradefects detection. However, it’s not only interfered by uncertain factors from testingengineers and testing environment seriously, but also lack of objective quantitativecriteria. Hence, the detection method applied to Mura now is extremely inefficient anddifficult to guarantee the quality of service, which leads to an urgent need to study thestable and efficient automatic detection method of Mura defects.A Mura defect detection method based on machine vision, which consists ofimage acquisition, image preprocessing, background suppression, uneven luminanceadjustment, image segmentation and defect quantization, is proposed in the dissertationaccording to the analysis of the typical features of Mura defects. Simultaneously, thedissertation achieves key solutions to the three critical problems of backgroundsuppression, uneven luminance adjustment and image segmentation through detailedtheoretical derivation, algorithm design and simulation, and finally accomplishes theestablishment of detecting procedure of Mura defects in TFT-LCD based on machinevision. To solve the problem of background suppression, an algorithm based on2D-Gabor filter is proposed and successfully suppresses the textures in image throughconfiguring the filter parameters appropriately, which makes the detection resultscloser to the criterion of the human eyes. To adjust the uneven luminance existing in the images of the samples, a proposal based on blind source separation is put forward,which regards uneven luminance and moire fringe as multiplicative noise andsuppresses it through the combination of the homomorphic transform and FastICAalgorithm without prior knowledge. To fulfill the segmentation of low contrast Muradefect, a scheme based on chan-vese model is performed with the help of level setmethod, which reaches the actually profile of Mura defect. At the same time, the areaand contrast of Mura defect are used in the quantization according to SEMU.Finally, experimental detection is done and48out of50TFT-LCD samples withMura defects are successfully detected. The experimental result shows that thedetection method proposed in the dissertation is practical feasible and manages to fulfillthe required.