Automatic identification of cashmere and wool fibers based on microscopic visual features and residual network model. (April 2021)

• Record Type: Journal Article
• Authors: Luo, Junli; Lu, Kai; Chen, Yonggang; Zhang, Boping
• Is Part Of: Micron. Volume 143(2021)
• Publication Date: 2021-04
• Subjects: Fiber identification -- Microscopic image -- Visual feature -- Convolutional neural network; Microscopy -- Periodicals; Electron Probe Microanalysis -- Periodicals; Microscopy -- Periodicals; Microscopie -- Périodiques; Microscopy; Periodicals; 502.82
• DOI: 10.1016/j.micron.2021.103023
• Languages: English
• ISSNs: 0968-4328

Abstract

Highlights: Transfer learning can improve the identification rate of convolutional neural network models on fiber images. Compared with other classic convolutional neural network models, ResNet18 model had a higher identification rate. The proposed method is able to effectively distinguish multiple classes of similar fibers. Abstract: Distinguishing cashmere and sheep wool fibers is a challenge. In this study, we propose a residual net-based method for the identification of cashmere and sheep wool fibers. First, optical microscopic images of six different types of cashmere and sheep wool fibers were collected, and then the sample images were data-augmented. Several classic convolutional neural network (CNN) models were trained and tested with the sample images. The comparison showed that the proposed residual net model with 18 weight layers had the highest accuracy, with an overall accuracy above 97.1 % on the test set; the highest accuracy on the Australian merino wool and Mongolian brown cashmere, both above 98 %; and the lowest accuracy on the Chinese white cashmere, above 95 %. The trained model exhibited a fast detection speed, processing 6000 sample images in less than 20 s.