Machine Vision Defect Detection And Classification Of Warp Knitted Fabrics

Defect detection has always been one of the most important components in the textile industry,and its effect is directly related to the production efficiency of enterprises.In order to improve the detection rate and accuracy of textile defects,more and more enterprises apply machine vision technology to fabric defects detection.Therefore,textile defect detection has been a research hotspot at home and abroad,and many new methods and technologies have emerged.However,due to the great difference of texture and many kinds of defects in different fabrics,there is no one method that can be applied to all defect detection.Therefore,this paper closely links with the production of enterprises,takes warp knitting as the experimental object,takes defect detection and classification as the experimental purpose,and studies the common defects of warp knitting,in order to find more efficient detection means.The main research work of this subject is as follows:First of all,According to the object and purpose of the experiment,two comparative defect detection schemes are proposed,which are space domain and frequency domain.This paper introduces the experimental object of this subject and gives the scoring standard of cloth as the basis for judging the quality of cloth in the follow-up.It establishes the overall thinking and framework of this paper.Secondly,Warp knitted fabric defects detection in space domain.Due to the lack of pertinence of traditional detection methods,this paper adopts a method of combining LBP with gray level co-occurrence matrix.Firstly,after a series of pretreatment processes,LBP algorithm is used to process warp knitting image,and then combined with gray level co-occurrence matrix,a series of feature parameters are extracted to describe the overall image information.These feature parameters will be the main basis for subsequent recognition and classification.Thirdly,defect detection of warp knitted fabric in frequency domain.Because the Gabor filter is stable and efficient,the Gabor filter is chosen as the frequency domain detection method.Firstly,the image is processed by improved multi-channel fusiontechnology,and then the binary image is segmented by thresholding.Finally,the eigenvalues of the binary image can be obtained by calculating.These eigenvalues will also be the main basis for subsequent judgment.Finally,comparing the two treatment methods which is better.Considering the difficulty,stability,accuracy and so on,the accuracy includes two aspects: the recognition of defects and the classification of defects,and the value of accuracy is obtained by K-neighboring algorithm.Through the comprehensive comparison of the two treatment methods,we can determine which treatment method is better for the common defects of warp knitted fabric.Finally,according to the severity of defects,cloth grading should be completed.