Vision AI System Development for Improved Productivity in Challenging Industrial Environments: A Sustainable and Efficient Approach

Autor: Changmo Yang, JinSeok Kim, DongWeon Kang, Doo-Seop Eom
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Applied Sciences, Vol 14, Iss 7, p 2750 (2024)
Druh dokumentu: article
ISSN: 14072750
2076-3417
DOI: 10.3390/app14072750
Popis: This study presents a development plan for a vision AI system to enhance productivity in industrial environments, where environmental control is challenging, by using AI technology. An image pre-processing algorithm was developed using a mobile robot that can operate in complex environments alongside workers to obtain high-quality learning and inspection images. Additionally, the proposed architecture for sustainable AI system development included cropping the inspection part images to minimize the technology development time, investment costs, and the reuse of images. The algorithm was retrained using mixed learning data to maintain and improve its performance in industrial fields. This AI system development architecture effectively addresses the challenges faced in applying AI technology at industrial sites and was demonstrated through experimentation and application.
Databáze: Directory of Open Access Journals
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