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 abo...