MobileViT model-based real-time fiber identification method for cashmere and wool

Abstract

The physical and morphological characteristics of wool and cashmere fibers exhibit notable similarities, making distinguishing them challenging. In this study, we propose a method based on a lightweight hybrid model called MobileViT, which combines a vision transformer and convolutional neural network, for the real-time identification of fiber categories. After training on a large sample dataset, the model was validated on a test set of 61,095 fiber images belonging to six categories; it took 26.2 s to achieve a recognition accuracy of 97.19%. This paper presents the first attempt to use a hybrid model of Transformer and Convolutional Neural Network (CNN) for the recognition of fiber images. Experimental results demonstrate that the model is capable of effectively extracting features from fibers, and it outperforms pure CNN models in terms of both speed and accuracy.