Pattern recognition and feature extraction methods are applied to identify cashmere and wool fibers, which are two kinds of very similar animal fibers. In this article, we proposed a new identification method based on Speed Up Robust Features of fiber images. The images of wool and cashmere fibers are obtained by scanning electron microscopy. Speed Up Robust Features of fiber images are extracted, and each fiber image is regarded as a collection of feature vectors in our logic. The vectors are fed into a support vector machine for supervised learning. The findings from scanning electron microscope images indicate that this method is effective; the recognition rate is higher than 93% for a broad range of blend proportions of the two fibers.

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Wool fiber and cashmere fiber are similar in physical and morphological characteristics. Thus, the identification of these two fibers has always been a challenging proposition. This study identifies five kinds of cashmere and wool fibers using a convolutional neural network model. To this end, image preprocessing was first performed. Then, following the VGGNet model, a convolutional neural network with 13 weight layers was established. A dataset with 50,000 fiber images was prepared for training and testing this newly established model. In the classification layer of the model, softmax regression was used to calculate the probability value of the input fiber image for each category, and the category with the highest probability value was selected as the prediction category of the fiber. In this experiment, the total identification accuracy of samples in the test set is close to 93%. Among these five fibers, Mongolian brown cashmere has the highest identification accuracy, reaching 99.7%. The identification accuracy of Chinese white cashmere is the lowest at 86.4%. Experimental results show that our model is an effective approach to the identification of multi-classification fiber.

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