Research On Fabric Classification Algorithm Based On Videos

With the rapid development of the textile industry,there are more and more types of textile fabrics,especially the development of new textile fiber materials and the generalization of mixed spinning fabrics.Fabric identification and detection has gradually become a research hotspot.In the textile industry,the identification and detection of fabrics also plays a vital role.Most of the traditional methods are to determine the type of fabric by burning test.During the detection process,according to the burning odor of the fibers in the fabric and the melting condition with the naked eye,the fabric materials are manually treated and the appropriate identification is made.These methods all require special testing personnel and are easily affected by human factors.In addition,due to the material composition of the unknown fabric,the material needs to be identified under the premise of destroying the fabric,which leads to a reduction in production efficiency and unnecessary waste.Therefore,there is an urgent need for a rapid,accurate,and suitable method for the classification and testing of popular fabrics.In order to solve the problems of fabric detection in the textile industry,this paper takes the data set of thirty kinds of fabrics under different wind forces as the research object,and proposes to combine the traditional textile industry with advanced computer technology to use the multi-frame of the swinging ground fabric Image,from two aspects of deep learning and fabric mechanical model to identify the fabric.The main work of this paper is divided into two parts: fabric recognition algorithm based on deep learning and fabric recognition algorithm based on mechanical model.(1)Based on video data sets of various types of fabrics swaying under different winds,analyze the fabric’s motion trajectory,construct a fabric’s motion trajectory data set,and characterize it,and select appropriate deep learning according to the characteristics of the data set The model is trained.First,the mainstream video classification deep neural network is selected to conduct an empirical study on the categories of a public fabric dataset.The experimental results on the same fabric database show that the performance of the dual-flow architecture neural network is the best.Secondly,the Two-Stream architecture neural network is improved.In the first fully connected layer,temporal and spatial convolutional neural networks are fused to improve the accuracy of fabric classification.In addition,this paper also uses transformable convolutions in residual networks to enhance the transform modeling capabilities of convolutional neural networks.Finally,the experimental results show that the improved Two-Stream + architecture has obvious advantages for fabric classification,and the accuracy rate is as high as 98.75%.(2)Aiming at the interaction force of the fabric’s knitting nodes under the action of wind,a fabric recognition algorithm based on a mechanical model,that is,a fabric mechanical model is proposed.The main goal of this study is to distinguish different fabrics based on the interaction forces of textile materials under wind force.First,the particle advection method was used to calculate the interaction force between the knitting nodes of the fabric.Secondly,the power flow image is divided into blocks and visual words are randomly extracted for clustering.Finally,a potential Dirichlet allocation model is used to output the likelihood estimates,and a line chart is constructed to classify the fabrics.The experiment proves that the fabric mechanical model can effectively classify fabrics.In addition,under different wind forces,the same fabric interacts with the same force,which provides the basis for the next fabric modeling and simulation work.In summary,this paper starts with two aspects of fabric recognition algorithms based on deep learning and fabric recognition algorithms based on mechanical models,and achieves accurate classification of fabrics through video of fabric swing.