The Application of Deep and Transfer Learning for Identifying Cashmere and Wool Fibers

Abstract

Identification of wool and cashmere fibers is one of the most important topics in the textile industry. In order to recognize these similar fibers, a novel identification method based on the convolution neural network and deep learning was proposed in this paper. As we all know, training a new identification network commonly requires lots of sample images and needs an amount of time, so the transfer learning was adopted for the fiber identification. The four pre-trained convolution neural networks, which consist of AlexNet, VGG-16, VGG-19, GoogLeNet, were used for the transfer learning to identify these similar fibers. Then, 65 fiber images of four kinds of fiber samples including goat hair, yellow wool, sheep wool, and cashmere fibers, were collected, respectively, and processed by the methods including random interception and rotation to obtain a total of 390 fiber images, respectively, for the experiment analysis. After comparing different network models, the results showed that the highest identification accuracy was 99.15%, obtained by the VGG-16 transfer learning model and the proportion of training set to testing set was 7:3. In addition, compared with the traditional machine learning algorithmics, this method also had a great improvement in the model performance and identification accuracy.

Keywords

• cashmere/wool identification
• deep learning
• transfer learning
• convolution neural networks