世界科学技术-中医药现代化2025，Vol.27Issue(2)：364-374,11.DOI:10.11842/wst.20231231001
基于改进YOLOv3的中药饮片智能鉴别模型研究
Intelligent Identification Model of Traditional Chinese Medicine Pieces Based on Improved YOLOv3 Algorithm
摘要
Abstract
Objective To improve the accuracy of intelligent detection and evaluation of traditional Chinese medicine(TCM)pieces and solve the problems of leakage,misdetection,inaccurate localization and low confidence in the study of TCM pieces identification,YOLOv3 algorithm which has good detection effect for high overlap and small targets was improved.Methods An RGB image database containing 148 commonly used TCM pieces was established.Based on the YOLOv3 algorithm model,the anchor box size was improved by K-means clustering algorithm.The CIoU loss function was introduced for bounding box regression to improve the localization accuracy and confidence of bounding boxes.The traditional non-maximum suppression was improved to DIoUNMS to reduce the problems of missed detection and false detection of dense targets with high overlap by YOLOv3 algorithm.Results 148 kinds of TCM pieces were tested with the improved algorithm,and the average detection accuracy of 98.47%was achieved,which is 1.83%better than the original YOLOv3 algorithm.It realizes better detection effect for TCM pieces in complex situations such as dense,high overlapping,etc.Problems such as leakage,misdetection,imprecise positioning and low confidence level have been alleviated to a certain extent.Conclusion The improved algorithm effectively improves the recognition accuracy and generalization ability of TCM pieces,providing a new reference for the realization of automated intelligent detection of TCM pieces.关键词
中药饮片/深度学习/YOLOv3/损失函数/非极大值抑制Key words
TCM pieces/Deep learning/YOLOv3/Loss function/Non-maximum suppression分类
医药卫生引用本文复制引用
高爽,周志强,钟思羽,黄显章..基于改进YOLOv3的中药饮片智能鉴别模型研究[J].世界科学技术-中医药现代化,2025,27(2):364-374,11.基金项目
国家农业农村部现代农业产业技术体系建设专项(CARS-21):山药焦作综合试验站,负责人:黄显章 （CARS-21）
南阳市科学技术局南阳市科技攻关计划基金项目(KJGG098):基于深度学习的中药饮片鉴别研究,负责人:高爽. （KJGG098）
