An Embedded System With YOLOv5 for Automated Drug Delivery System
VERSION OF RECORD ONLINE: 17/09/2025
Corressponding author's email:
tstoan1512@tvu.edu.vnDOI:
https://doi.org/10.54644/jte.2025.1758Keywords:
YOLOv5, Raspberry Pi, Automated Drug Delivery, Embedded System, PLCAbstract
The integration of technology into pharmaceutical operations has led to the development of automated drug delivery systems, bringing numerous benefits such as reducing medication errors and improving patient satisfaction. With advancements in technology, automated drug delivery systems have a huge growth potential. Their deployment can significantly improve healthcare services and drive the development of the pharmaceutical industry. In this study, an embedded system on Raspberry Pi integrated with the YOLOv5 deep learning model and a hardware system controlled by a Mitsubishi FX5U Programmable Logic Controller (PLC) is proposed for a drug dispensing system. Drug vials will be collected and their images analyzed by YOLOv5, and a proposed line cutting position determination algorithm will identify the necessary cutting positions. These positions will be communicated to the PLC and control the cutting system accordingly. The training results of the YOLOv5 model achieved an accuracy of over 99% for basic drug types. The optimal cutting path determination algorithm provides the correct cutting positions to the cutting system from the PLC. The research results contribute to the construction and development of automated drug dispensing devices and systems.
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References
K. S. Yadav, S. Kapse-Mistry, G. J. Peters, and Y. C. Mayur, “E-drug delivery: a futuristic approach,” Drug Discov. Today, vol. 24, no. 4, pp. 1023–1030, Apr. 2019, doi: 10.1016/j.drudis.2019.02.005.
M. F. Alanazi et al., “Impact of automated drug dispensing system on patient safety,” Pharm. Pract., vol. 20, no. 4, pp. 01–11, Dec. 2022, doi: 10.18549/PharmPract.2022.4.2744.
M. K. Chakravarthi, B. Bharath, and R. V. Sreehari, “Implementation of an automated drug delivery system using linear actuator,” in 2015 International Conference on Soft Computing Techniques and Implementations (ICSCTI), Faridabad, India: IEEE, Oct. 2015, pp. 90–93. doi: 10.1109/ICSCTI.2015.7489571.
R. Sharma, D. Singh, P. Gaur, and D. Joshi, “Intelligent automated drug administration and therapy: future of healthcare,” Drug Deliv. Transl. Res., vol. 11, no. 5, pp. 1878–1902, Oct. 2021, doi: 10.1007/s13346-020-00876-4.
H. Zhao, “Applications of Embedded Systems in Medicine: Challenges and Future Trends,” Highlights Sci. Eng. Technol., vol. 62, pp. 31–35, Jul. 2023, doi: 10.54097/hset.v62i.10416.
N. Arandia, J. I. Garate, and J. Mabe, “Embedded Sensor Systems in Medical Devices: Requisites and Challenges Ahead,” Sensors, vol. 22, no. 24, p. 9917, Dec. 2022, doi: 10.3390/s22249917.
Abhimanyu S Dhapola, “Embedded Systems and its Application in Medical Field,” 2015, doi: 10.13140/2.1.1299.1528.
M. Çakır, M. Ekinci, E. B. Kablan, and M. Şahin, “AVD-YOLOv5: a new lightweight network architecture for high-speed aortic valve detection from a new and large echocardiography dataset,” Med. Biol. Eng. Comput., vol. 62, no. 8, pp. 2511–2528, Aug. 2024, doi: 10.1007/s11517-024-03090-3.
B. Wu, C. Pang, X. Zeng, and X. Hu, “ME-YOLO: Improved YOLOv5 for Detecting Medical Personal Protective Equipment,” Appl. Sci., vol. 12, no. 23, p. 11978, Nov. 2022, doi: 10.3390/app122311978.
K. Jiang, S. Pan, L. Yang, J. Yu, Y. Lin, and H. Wang, “Surgical Instrument Recognition Based on Improved YOLOv5,” Appl. Sci., vol. 13, no. 21, p. 11709, Oct. 2023, doi: 10.3390/app132111709.
B. Aldughayfiq, F. Ashfaq, N. Z. Jhanjhi, and M. Humayun, “YOLO-Based Deep Learning Model for Pressure Ulcer Detection and Classification,” Healthcare, vol. 11, no. 9, p. 1222, Apr. 2023, doi: 10.3390/healthcare11091222.
S. Bashir, R. Qureshi, A. Shah, X. Fan, and T. Alam, “YOLOv5-M: A Deep Neural Network for Medical Object Detection in Real-time,” in 2023 IEEE Symposium on Industrial Electronics & Applications (ISIEA), Kuala Lumpur, Malaysia: IEEE, Jul. 2023, pp. 1–6. doi: 10.1109/ISIEA58478.2023.10212322.
F. Mo et al., “PLC orchestration automation to enhance human–machine integration in adaptive manufacturing systems,” J. Manuf. Syst., vol. 71, pp. 172–187, Dec. 2023, doi: 10.1016/j.jmsy.2023.07.015.
D. Pullaiah, S. GunaSekaran, T. Jerry Alexander, and N. R. Krishnamoorthy, “Development of PLC Program for Multi-Process Parameter and Multi Profile-based Control Logic for Heat Treatment Industrial Applications,” J. Phys. Conf. Ser., vol. 1770, no. 1, p. 012038, Mar. 2021, doi: 10.1088/1742-6596/1770/1/012038.
R. R. Vicerra et al., “Implementation of a Programmable Logic Controller (PLC)-Based Control System for a Bag-Valve-Mask-Based Emergency Ventilator,” in 2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), Manila, Philippines: IEEE, Dec. 2020, pp. 1–5. doi: 10.1109/HNICEM51456.2020.9400087.
G. Jocher et al., ultralytics/yolov5: v7.0 - YOLOv5 SOTA Realtime Instance Segmentation. (Nov. 22, 2022). Zenodo. doi: 10.5281/ZENODO.3908559.
A. Paszke et al., “PyTorch: An Imperative Style, High-Performance Deep Learning Library,” 2019, arXiv. doi: 10.48550/ARXIV.1912.01703.
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