结合改进胶囊网络与知识蒸馏的茶青分类方法研究

doi:10.13305/j.cnki.jts.2022.03.002

Abstract

Abstract: The accurate classification of different grades of tea buds is very important for the development of the famous tea industry. The use of traditional sensory evaluation methods for sorting makes the results subjective. In this research, a data set was established after tea leaf images were collected, and a new network model, GA-CapsNet, was proposed by combining the ghost attention bottleneck and capsule network. The model was trained by the method of growing knowledge distillation based on the linear decay scaling coefficient, while migrating the parameter matrix of teacher model, the student model was adaptively reduced with iteration. The experimental result shows that, compared with other similar algorithms, the proposed method had excellent classification performance on small-scale data sets. The accuracy, recall and F1-score were 94.97%, 95.51% and 95.24%, respectively. Here, a GA-CapsNet model based on machine vision and deep learning technology was established, which provided a new idea for solving the tea leaf classification problem.

Key words: capsule network, knowledge distillation, attention module, tea bud grading

CLC Number:

CHEN Xingran, HUANG Haisong, HAN Zhenggong, FAN Qingsong, ZHU Yunwei, HU Pengfei. Research on the Classification Method of Tea Buds Combining Improved Capsule Network and Knowledge Distillation[J]. Journal of Tea Science, 2022, 42(3): 387-396.

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Research on the Classification Method of Tea Buds Combining Improved Capsule Network and Knowledge Distillation

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