Identification and Separation of Medicine Through Robot Using YOLO and CNN Algorithms for Healthcare

By streamlining medicine recognition and separation, the incorporation of artificial intelligence (AI) has transformed industrial processes, particularly in conveyor belt systems by Robot. These advancements are crucial for boosting productivity and maintaining quality standards. In contrast, older...

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Bibliographic Details
Published in:	2023 International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI) Vol. 1; pp. 1 - 5
Main Authors:	Patel, Govind S., Desai, Ashish A., Kamble, Yogesh Y., Pujari, Ganesh V., Chougule, Priyanka A., Jujare, Varsha A.
Format:	Conference Proceeding
Language:	English
Published:	IEEE 29-12-2023
Subjects:	Automation Camera Conveyer belt DSP processor Filter Industries Medical services Medicine PLC Productivity Robot Service robots Technological innovation YOLO
Online Access:	Get full text
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Summary:	By streamlining medicine recognition and separation, the incorporation of artificial intelligence (AI) has transformed industrial processes, particularly in conveyor belt systems by Robot. These advancements are crucial for boosting productivity and maintaining quality standards. In contrast, older technologies were rudimentary and lacked the capabilities of today's AI, DSP processors, and advanced PLC systems. These earlier techniques weren't as accurate, needed more manual work, and weren't as flexible as contemporary approaches. Although they served a purpose, the effectiveness, accuracy, and adaptability of modern automation systems much outweigh their advantages. With the goal to cope with item separation and recognition, this paper recommends a reliable approach designed specifically for conveyor belt systems in AI applications using YOLO and CNN algorithm. The method makes employing hardware acceleration and real-time processing while taking security precautions to guarantee secure medicine handling. The proposed approach has undergone rigorous real-world testing, demonstrating its dependability, potency, and adaptability. The outcomes demonstrated how the algorithm is competent at handling intricate conveyor belt scenarios with minimal complexity and providing prompt solutions. Given its adaptability, the algorithm can be used across a wide range of industries, boosting production, reducing costs, and improving quality control.
DOI:	10.1109/ICAIIHI57871.2023.10489407