摘要
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.
作者
林强强
金守峰
马秋瑞
LIN Qiangqiang;JIN Shoufeng;MA Qiurui(College of Mechanical and Electrical Engineering, Xi'an Polytechnic University , Xi'an 710048, China;Key Laboratory of Modern Intelligent Textile Equipment, Xi'an Polytechnic University, Xi'an 710048, China;College of Fashion and Art of Design, Xi'an Polytechnic University, Xi'an 710048, China)
基金
National Natural Science Foundation of China(No.61701384)
Natural Science Basic Research Plan in Shaanxi Province of China(No.2017JM5141)
Shaanxi Provincial Education Department,China(No.17JK0334)
Xi'an Polytechnic University Graduate Innovation Fund,China(No.chx2019083)
Science Foundation for Doctorate Research of Xi'an Polytechnic University,China(No.BS1535)
Key Research and Development Program of Shaanxi,China(No.2020GY-172)
Technology Innovation Leading Program of Xi'an,China(No.201805030YD8CG14(5))
Xi'an Key Laboratory of Modern Intelligent Textile Equipment,China(No.2019220614SYS021CG043)。
