Recognition of cashmere and wool fiber based on improved B-CNN model

Abstract

Due to the small inter-class differences of the object itself and the larger intra-class differences caused by the shooting environment and background, the image recognition of cashmere and wool has always been a problem in the textile field. In order to solve the problem, an improved bilinear convolutional neural network model for cashmere and wool fiber recognition was proposed. The two-way network of the B-CNN model was improved to extract feature vectors of different levels of fiber original sample images and skeleton images, and the features of the two images were fused using vector stitching in this method, so as to complement information and enhance the ability of feature expression. Finally, transfer training was used to solve the problem of small samples of fiber images and improve classification accuracy and efficiency. The experimental results show that the test set accuracy of this model can be up to 98.06% as opposed to that of the classic B-CNN model. It shows that the model can effectively solve the problem of cashmere and wool fiber recognition.

Keywords

• cashmere
• wool
• bilinear convolutional neural network model
• feature fusion
• transfer training