Thin-film transistor liquid crystal display surface micro-defects are difficult to be detected using traditional threshold or edge detection methods. This article puts forward a non-destructive detection method using particle swarm optimization with one-class support vector machine to inspect thin-film transistor liquid crystal display surface micro-defects. An image acquisition system is constructed to acquire the surface micro-defects images of thin-film transistor liquid crystal display. Background textures are removed by the image preprocessing algorithm based on one-dimensional discrete Fourier transform. Moreover, the wavelet transform algorithm is used to eliminate the influence of uneven illumination. Effective characteristic parameters describing thin-film transistor liquid crystal display surface micro-defects are selected by the principal component analysis method. Classification model is developed based on one-class support vector machine using radial basis function. To validate the method above, other parameter optimization algorithms, including normal algorithm, genetic algorithm, and grid search algorithm, are used to optimize the support vector machine model parameters: penalty parameter C and kernel parameter g. In contrast, particle swarm optimization is proved to get the optimal model parameter, and the recognition accuracy of 91.7% is obtained from the particle swarm optimization-oneclass support vector machine model. The results indicate the proposed system and method can accurately inspect thinfilm transistor liquid crystal display surface detects.
The wide application of intelligent manufacturing technologies imposes higher requirements for the quality inspection of industrial products; however, the existing industrial product quality inspection methods generally have a few shortcomings such as requiring many inspectors, too complicated methods, difficulty in realizing standardized monitoring, and the low inspection efficiency, etc. Targeting at these problems, this paper proposed an automatic detection and online quality inspection method for workpiece surface cracks based on the machine vision technology. At first, it proposed a vision-field environment calibration method, gave the specific method for workpiece shape feature recognition and size measurement based on machine vision, and achieved the on-line monitoring of workpiece quality problems such as feature defects and size deviations. Then, this study integrated the multi-scale attention module and the up-sampling module that can restore the locations of image pixels based on the high-level and low-level hybrid feature maps, built a workpiece crack extraction network, and realized workpiece crack feature extraction, crack type classification, and damage degree division. At last, experimental results verified the effectiveness of the proposed method, and this paper provided a reference for the application of machine vision technology in other fields.
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