Identification of Chinese Herb Pieces Based on YOLOv4
10.13422/j.cnki.syfjx.20230614
- VernacularTitle:基于YOLOv4算法的中药饮片识别
- Author:
Cong GUO
1
;
Yujia TIAN
1
;
Yang LI
2
;
Yang LIU
1
;
Jun ZHANG
1
;
Jipeng DI
1
;
Aixia YAN
3
;
An LIU
1
Author Information
1. Institute of Chinese Materia Medica， China Academy of Chinese Medical Sciences，Beijing 100700，China
2. College of Intelligence and Information Engineering， Shandong University of Traditional Chinese Medicine， Jinan 250355，China
3. College of Life Science and Technology， Beijing University of Chemical Technology， Beijing 100029，China
- Publication Type:Journal Article
- Keywords:
Chinese herbal pieces;
convolutional neural networks;
deep learning;
image recognition;
object detection
- From:
Chinese Journal of Experimental Traditional Medical Formulae
2023;29(14):133-140
- CountryChina
- Language:Chinese
-
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.
