Automatic inspection and classification for thin-film transistor liquid crystal display surface defects based on particle swarm optimization and one-class support vector machine

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–one-class support vector machine model. The results indicate the proposed system and method can accurately inspect thin-film transistor liquid crystal display surface detects.