OLED Defective Pixel Recognition Based on K-means Clustering and LVQ Neural Network

doi:10.3969/j.issn.1006-2475.2019.07.007

Abstract

Abstract: An improved K-means clustering segmentation and LVQ neural network classification method is proposed to identify defective pixels in inkjet printing process of organic light-emitting diode display panels. Firstly, the improved K-means clustering algorithm is used to segment the preprocessed printed pixels, then the connected-domain horizontal rectangle is used to determine the coordinates and geometric features of each printed pixel, and the texture features are extracted by the gray-scale co-occurrence matrix, finally, the features are classified by LVQ neural network to complete the marking and classification statistics of the defective pixels. The results show that the proposed algorithm recognition rate of the article is obviously better than other common classification recognition algorithms, the average defect detection rate of the algorithm is 100%, the classification accuracy rate is 98.9%, and the single pixel detection time is 8.3 ms.

Key words: organic light-emitting diode, pixel defect, K-means clustering, connected domain, gray level co-occurrence matrix, LVQ neural network

CLC Number:

TP391.4

JI Yan-ling1, LIN Zhi-xian1, TANG Qian1, GUO Tai-liang1, TANG Biao2. OLED Defective Pixel Recognition Based on K-means Clustering and LVQ Neural Network[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2019.07.007.

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