| Summary: | 碩士 === 元智大學 === 工業工程與管理學系 === 91 === The surface defects in TFT-LCD array panels generally can be categorized as macro- and micro-defects. Macro-defects are large in size and can be visually identified by human inspectors. However, sizes of micro-defects are generally very small and cannot be easily detected by human personnel. In this study, we propose an automatic visual system for defect detection and classification, and especially focus on micro-defects in TFT-LCD array panels in the manufacturing process. Micro-defects of TFT-LCD array panels investigated in this study include pinholes, particles, scratches and fingerprints.
Since a TFT-LCD array panel comprises repetitive vertical and horizontal line patterns, it can be considered as a homogeneously structural texture. The proposed method does not rely on local features of textures. It is based on a global image reconstruction scheme using the 2D Fourier transform. The Fourier spectrum is ideally suited for describing the directionality and periodicity of line patterns in a TFT-LCD image. By eliminating the frequency components that represent the background texture of the TFT-LCD surface, and back transforming the image using the inverse Fourier transform, we can effectively remove the repetitive line patterns and enhance local defects in the reconstructed image. The geometric features of each detected defect in the reconstructed image are then extracted for further classification. Experimental results from a variety of TFT-LCD array samples have shown the efficacy of the proposed method for detecting and classifying micro-defects.
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