基于eIQ的中药材图像识别系统的设计与实现  

Design and implementation of image recognition system for Chinese medicinal materials based on eIQ

作　　者：韩德强 李宗耀 杨淇善 高雪园 Han Deqiang;Li Zongyao;Yang Qishan;Gao Xueyuan(Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China)

机构地区：[1]北京工业大学信息学部,北京100124

出　　处：《电子技术应用》2023年第10期118-123,共6页Application of Electronic Technique

摘　　要：中药材对人体疾病的预防及控制具有重要的作用,然而普通百姓对中药材知识了解过少,可能由于滥用中药材而带来不可控的后果。因此,对中药材进行精准识别是一项紧迫的任务。将轻量级神经网络模型应用到中药材识别中,提出在微控制器上实现基于MobileNetV3模型的中药材图像识别系统。首先建立中药材图像数据集,在eIQ机器学习软件开发环境中根据MobileNetV3构建识别基础模型,并通过调整模型参数实现对模型的优化,最后将模型文件部署到i.MX RT1060上,实现了对30种中药材的识别。最终在验证集的准确率达到86.79%。结果表明,在i.MX RT1060上实现中药材识别具有很好的实际效果。Chinese herbal medicines play an important role in the prevention and control of human diseases,but the general public's knowledge of Chinese medicinal materials is too little,which may bring uncontrollable consequences due to the abuse of Chinese medicinal materials.Therefore,the accurate identification of Chinese medicinal materials is an urgent task.In this paper,the lightweight neural network model is applied to the recognition of Chinese medicinal materials,and an image recognition system based on the MobileNetV3 model is proposed on a microcontroller.Firstly,the image dataset of Chinese medicinal materials is established,the recognition basic model is built according to MobileNetV3 in the eIQ machine learning software development environment,and the model is optimized by adjusting the model parameters,and finally the model file is deployed to i.MX RT1060.Image recognition of 30 kinds of Chinese medicinal materials was realized,and the accuracy rate in the verification set reached 86.79%.The results showed that the image recognition of Chinese medicinal materials on i.MX RT1060 has a good practical effect.

关 键 词：微控制器 中药材识别 MobileNetV3 卷积神经网络 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]