Identification of wool and cashmere fibers based on multiscale geometric analysis

The identification of wool and cashmere fibers has always been an essential topic in the field of textile. In this work, a fiber recognition algorithm based on multiscale geometric analysis and deep convolutional neural network is introduced. As a new tool for signal analysis with multiscale and multi-direction, curvelet transform not only has multiscale and multi-resolution characteristics of the wavelet transform, but also has very strong directionality in the sense that it can provide an optimally sparse representation of fiber image with a large number of fiber contours information. The proposed method was based on multiscale geometric analysis to reduce the dimension of wool/cashmere fiber images and reduce the calculation of redundant data. The deep convolutional neural network was used to classify and recognize wool/cashmere fiber images. Then 400 fiber images of two kinds of fiber samples including wool and cashmere fibers, were collected respectively and processed by the methods including random interception and rotation to obtain a total of 600 fiber images respectively for the experiment analysis. The results show that the recognition accuracy is up to 96.67%. Compared with the traditional feature extraction and classification algorithm, this method dramatically improves model performance and identification accuracy.