中圖分類號：TP183 文獻(xiàn)標(biāo)志碼：A DOI: 10.16157/j.issn.0258-7998.233788 中文引用格式： 韓德強(qiáng),，李宗耀,，楊淇善,，等. 基于eIQ的中藥材圖像識別系統(tǒng)的設(shè)計與實現(xiàn)[J]. 電子技術(shù)應(yīng)用,，2023,，49(10)：118-123. 英文引用格式： Han Deqiang,，Li Zongyao,，Yang Qishan,，et al. Design and implementation of image recognition system for Chinese medicinal materials based on eIQ[J]. Application of Electronic Technique,，2023,，49(10)：118-123.
Design and implementation of image recognition system for Chinese medicinal materials based on eIQ
Han Deqiang，Li Zongyao,，Yang Qishan,，Gao Xueyuan
(Faculty of Information Technology,，Beijing University of Technology,，Beijing 100124,，China)
Abstract： 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.
Key words : MCU,；identification of Chinese herbal medicines,；MobileNetV3；convolutional neural network
目前,，中藥材的智能識別主要依靠復(fù)雜的深度神經(jīng)網(wǎng)絡(luò)實現(xiàn),。其中，吳沖等利用人工智能和機(jī)器視覺技術(shù)設(shè)計出一種檢測貝母,、山楂及半夏飲片質(zhì)量方法[2],。張志光通過向YOLO4目標(biāo)檢測算法中加入Non-local注意力機(jī)制和RFB（Receptive Field Block，增強(qiáng)感受野）模塊來提升算法在復(fù)雜背景和不同尺度下中藥飲片的識別性能[3],。徐飛等通過強(qiáng)化特征提取改進(jìn)的AlexNet模型對5類中草藥葉片進(jìn)行訓(xùn)練并通過增廣數(shù)據(jù)集,，提高了中草藥圖像分類的準(zhǔn)確率[4]。李鑫利用Faster- RCNN算法對黃芪,、白術(shù),、白芷、白芨,、西洋參五種藥材進(jìn)行訓(xùn)練并搭建了中藥飲片圖像識別模型[5],。
由于借助神經(jīng)網(wǎng)絡(luò)實現(xiàn)中藥材識別需要大量的矩陣運(yùn)算、存儲空間和功耗,，因此大多依賴圖形處理器（Graphic Processing Unit,，GPU）或服務(wù)器實現(xiàn)，不但成本較高,，而且在實際使用中非常不便,。然而微控制器（Microcontroller Unit, MCU）卻具有體積小、功耗低,、成本低以及高實時性的優(yōu)勢,。并且隨著輕量級神經(jīng)網(wǎng)絡(luò)模型和擁有高性能、高主頻且包含有算力擴(kuò)展的Cortex-M7內(nèi)核的MCU的出現(xiàn),，使得在MCU平臺上實現(xiàn)中藥材識別變?yōu)榱丝赡堋?/p>
