An Embedded System With YOLOv5 for Automated Drug Delivery System

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.