Drug Appearance Recognition Based on Object Detection
10.13748/j.cnki.issn1007-7693.20223110
- VernacularTitle:基于目标检测的药品外观识别
- Author:
Xiaoyu ZHANG
1
;
Jianzhi DENG
1
;
Jun LUO
2
;
Jiaqing XU
1
Author Information
1. School of Physics and Electronic Information Engineering, Guilin University of Technology, Guilin 541004, China
2. Department of Pharmacy, First Affiliated Hospital of Guangxi Medical University, Nanning 530021, China
- Publication Type:Journal Article
- Keywords:
target detection； YOLOv4 ； drug appearance recognition ； GhostNet ；coordinate attention；Bi-directional Feature Pyramid Network
- From:
Chinese Journal of Modern Applied Pharmacy
2024;41(7):983-989
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE :In the process of drug dispensing, using computer vision technology to identify drugs is vulnerable to the influence of lighting, angle, packaging and other factors, which will produce large identification errors. Therefore, this paper proposes an object detection algorithm for drug appearance recognition(YOLOv4-GhostNet-CMB).
METHODS
Firstly, the algorithm redesigned the backbone feature extraction network in YOLOv4 by using GhostNet. Secondly, the CA attention model was brought into the Ghost module, aggregate features along horizontal and vertical directions to enhance the precise positioning of drugs. Finally, Bi-FPN feature pyramid structure was introduced to connect with the new backbone, and added a feature graph output which could enhance feature extraction and improved the detection accuracy of drugs.
RESULTS
The experimental results show that the average detection accuracy of YOLOv4-GhostNet-CMB algorithm reached 92.24%, which was a significant improvement of 4.49% compared with YOLOv4 algorithm in term of detection accuracy.
CONCLUSION
The model size is only 150 MB, nd this algorithm can effectively identify drugs.
