Intelligent Identification Model of Traditional Chinese Medicine Pieces Based on Improved YOLOv3 Algorithm
- VernacularTitle:基于改进YOLOv3的中药饮片智能鉴别模型研究
- Author:
Shuang GAO
1
;
Zhiqiang ZHOU
;
Siyu ZHONG
;
Xianzhang HUANG
Author Information
- Publication Type:Journal Article
- Keywords: TCM pieces; Deep learning; YOLOv3; Loss function; Non-maximum suppression
- From: World Science and Technology-Modernization of Traditional Chinese Medicine 2025;27(2):364-374
- CountryChina
- Language:Chinese
- 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.
