Scarf Defect Detection Method Based on Periodicity of Braided Texture

Abstract

Abstract The pure color scarf image is a typical periodic texture image. Based on the periodicity of scarf image texture, in order to eliminate the influence of periodical change of gray value of scarf image on scarf defect detection caused by periodic texture change, a defect detection method based on scarf knitting texture periodicity is proposed. Firstly, autocorrelation function is applied to determine the texture period of the averaged image, and the texture period is taken as the parameter to improve the quality of scarf defect detection Row down sampling and bilinear interpolation are used to eliminate the periodic change of gray value caused by texture. After that, the method based on maximum inter class variance is used to cut the image to complete the defect detection of scarf image. Through a large number of experiments, it is proved that this method has good effect on scarf defect detection, and has the characteristics of stable effect and strong practicability.