Identification of Chinese Herb Pieces Based on YOLOv4

GUO Cong; TIAN Yujia; LI Yang; LIU Yang; ZHANG Jun; DI Jipeng; YAN Aixia; LIU An

doi:10.13422/j.cnki.syfjx.20230614

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Identification of Chinese Herb Pieces Based on YOLOv4

更新时间：2023-06-20

- Identification of Chinese Herb Pieces Based on YOLOv4
- Chinese Journal of Experimental Traditional Medical Formulae Vol. 29, Issue 14, Pages: 133-140(2023)
- 作者机构：
  
  1.中国中医科学院中药研究所，北京 100700
  2.北京化工大学生命科学与技术学院，北京 100029
  3.山东中医药大学智能与信息工程学院，济南 250355
- 作者简介：
- 基金信息：
- DOI：10.13422/j.cnki.syfjx.20230614
  CLC： R284.2;R285;R289;R287;R22;R2-031;R33;R24
- Received：06 February 2023，
  
  Published Online：06 May 2023，
  
  Published：20 July 2023
- 稿件说明：
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郭丛,田钰嘉,李杨等.基于YOLOv4算法的中药饮片识别[J].中国实验方剂学杂志,2023,29(14):133-140.

GUO Cong,TIAN Yujia,LI Yang,et al.Identification of Chinese Herb Pieces Based on YOLOv4[J].Chinese Journal of Experimental Traditional Medical Formulae,2023,29(14):133-140.
郭丛,田钰嘉,李杨等.基于YOLOv4算法的中药饮片识别[J].中国实验方剂学杂志,2023,29(14):133-140. DOI： 10.13422/j.cnki.syfjx.20230614.

GUO Cong,TIAN Yujia,LI Yang,et al.Identification of Chinese Herb Pieces Based on YOLOv4[J].Chinese Journal of Experimental Traditional Medical Formulae,2023,29(14):133-140. DOI： 10.13422/j.cnki.syfjx.20230614.

摘要

中药饮片是中医药体系的重要组成部分，中药饮片的优劣识别及质量评级可促进其发展利用。利用深度学习对中药饮片进行智能识别，则在省时省力节约成本的前提下，合理避免了人为主观因素的制约，为中药饮片的高效识别提供了保障。该研究构建了包含108种中药饮片的数据集（14 058张图片），利用经典的YOLOv4算法对108种中药饮片建立了目标检测模型，模型的平均识别精度（mAP）为85.3%。此外，该研究也将感受野模块（RFB）添加至经典的YOLOv4算法的颈部网络，并利用改进后的YOLOv4算法对108种中药饮片进行计算预测。改进后的YOLOv4模型的mAP达到88.7%，对80种饮片的识别精度超过80%，对48种饮片的识别精度超过90%。此结果说明增加感受野模块可在一定程度上助于尺寸各异且体积较小的中药饮片的识别。最后，该研究分析了改进后的YOLOv4模型对于每类中药饮片的识别精度，通过对预测精度较低的中药饮片原始照片的深入分析，明晰了中药饮片原始照片的数量和质量是对此进行智能识别任务关键。该研究中构建的改进后的YOLOv4模型可用于中药饮片的快速识别，也为中药饮片的人工鉴伪工作提供可参考性的指引建议。

Abstract

Chinese herbal piece is an important component of the traditional Chinese medicine （TCM） system， and identifying their quality and grading can promote the development and utilization of Chinese herbal pieces. Utilizing deep learning for intelligent identification of Chinese herbal pieces can save time， effort， and cost， while also reasonably avoiding the constraints of human subjectivity， providing a guarantee for efficient identification of Chinese herbal pieces. In this study， a dataset containing 108 kinds of Chinese herbal pieces （14 058 images） was constructed，the basic YOLOv4 algorithm was employed to identify the 108 kinds of Chinese herbal pieces of our database The mean average precision （mAP） of the developed basic YOLOv4 model reached 85.3%. In addition， the receptive field block was introduced into the neck network of YOLOv4 algorithm， and the improved YOLOv4 algorithm was used to identify Chinese herbal pieces. The mAPof the improved YOLOv4 model achieved 88.7%， the average precision of 80 kinds of decoction pieces exceeded 80%， the average precision of 48 kinds of decoction pieces exceeded 90%. These results indicate that adding the receptive field module can help to some extent in the identification of Chinese herbal medicine pieces with different sizes and small volumes. Finally， the average precision of each kind of Chinese herbal medicine piece by the improved YOLOv4 model was further analyzed. Through in-depth analysis of the original images of Chinese herbal medicine pieces with low prediction average precision， it was clarified that the quantity and quality of original images of Chinese herbal medicine pieces are key to performing intelligent object detection. The improved YOLOv4 model constructed in this study can be used for the rapid identification of Chinese herbal pieces， and also provide reference guidance for the manual authentication of Chinese herbal medicine decoction pieces.

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references

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