ArticleEvidence-based complementary and alternative medicine : eCAM2022
Traditional Chinese Medicine Recognition Based on Target Detection.
Article in Evidence-based complementary and alternative medicine : eCAM, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Plasmonic artificial inspector for herbal medicines via surface-enhanced Raman spectroscopy and deep learning.Scientific reports · 2026Article
- Detection of submarine pipeline and cable targets based on depth feature of high resolution sonar image.PloS one · 2026Article
- Research on real-time detection and staging technology for pressure injuries in critically ill patients based on the YOLOv8 deep learning model.Frontiers in public health · 2026Article
- Deep learning-based classification of traditional Chinese medicine: a novel approach.Quantitative imaging in medicine and surgery · 2025Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Traditional Chinese medicine (TCM) is widely used in China, but the large variety can easily lead to difficulties in visual identification. This study aims to evaluate the availability of target detection models to identify TCMs. We have collected images of 100 common TCMs in pharmacies, and use three current mainstream target detection models: Faster RCNN, SSD, and YOLO v5 to train the TCM dataset. By comparing the metrics of the three models, the results show that the YOLO v5 model has obvious advantages in the recognition of a variety of TCM, the mean average accuracy of the YOLO v5 is 94.33% and the FPS has reached 75, this model has a smaller number of parameters and solves the problem of detection and occlusion for small targets. Our experiments prove that the target detection technology has broad application prospects in the detection of TCM.
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Registered trials
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