Objective evaluation of pilling of woven fabrics based on deep learning
At present,China's textile production is huge,and textile import and export trade affects the domestic economic development.Now,the most prominent problem of textiles is quality,of which fabric pilling condition is an extremely important part.The domestic assessment of pilling grade is mainly carried out by professional scientific personnel in some specific scenarios with the standard pilling samples for comparison,for which there are many limitations.First of all,the rating results may be affected by the subjective influence of the testers.Secondly,this method is time-consuming and costly,so there is a need for an objective rating system to grade the fabric pilling.In recent years,foreign scholars have began to use the computer to process some traditional images by extracting such parameters as the size,number,density,volume and other parameters of the pilling through traditional image processing methods,and such parameters can be used for fabric pilling rating.With the development of deep learning,high accuracy,convenience and other advantages are becoming increasingly prominent and they are widely used by domestic researchers.In the image field,feature extraction by convolutional neural network is effective and avoids the problem of subjective feature extraction.Therefore,we proposed a new Wide-SqueezeNet network for objective rating of fabric pilling based on deep learning.In this paper,two kinds of woven fabrics with different compositions and contents were used as samples,a ball-box pilling instrument was used to obtain different grades of pilling samples,and the fabrics were put under the light source for image acquisition by using a grayscale camera.A total of 4,376 samples of both kinds were collected.As for the network model,SqueezeNet with fewer parameters and fast training was used as the main body of the network which was innovatively designed.The network model has ten layers,but the number of parameters is small,so the expression ability of complex problems is weak.The new Wide-Fire module was formed by adding a short connection to the original Fire module and using two 3×3 small convolutions to obtain the information of the pilling feature map at different scales and fusing the features with the output of the original Fire module,while using a depth-separable convolution to replace the ordinary convolution in the network to reduce the computation to increase the training speed.A Wide-SqueezeNet network model with deep separable convolution was finally designed.After the training,the accuracy of Wide-SqueezeNet with improved Fire module is 2%higher than that of the base network,and the accuracy of Wide-SqueezeNet with deep separable convolution is increasedby 0.5%and the speed is improved.The final network model is significantly more accurate than some classical classification network models.Two 3×3 convolutional kernels and one 5×5 convolutional kernel are used for training,and the results show that the accuracy of the network with two 3×3 convolutional kernels is higher,so the two 3×3 convolutions are used for feature extraction.The experimental results show that the improvements in this paper improve the accuracy of the network classification,and the model size and computational effort are basically the same compared to the original network.Finally,the reliability of the network is further verified by the feature map and heat map of the network output,which proves that Wide-SqueezeNet is reliable in the objective rating method of woven fabric pilling.The comprehensive evaluation shows that the network model proposed in this paper can meet the requirements of pilling rating in the fabric industry.
国家科技期刊平台
NSTL
万方数据
维普
