Method for Detecting Fluff Quality of Fabric Surface Based on Support Vector Machine

In order to improve the accuracy of using visual methods to detect the quality of fluff fabrics, based on the previous research, this paper proposes a method of rapid classification detection using support vector machine(SVM). The fabric image is acquired by the principle of light-cut imaging, and the region of interest is extracted by the method of grayscale horizontal projection. The obtained coordinates of the upper edge of the fabric are decomposed into high frequency information and low frequency information by wavelet transform, and the high frequency information is used as a data set for training. After experimental comparison and analysis, the detection rate of the SVM method proposed in this paper is higher than the previously proposed back propagation(BP) neural network and particle swarm optimization BP(PSO-BP) neural network detection methods, and the accuracy rate can reach 99.41%, which can meet the needs of industrial testing.