20, May 2025

Smart Portable UV Sterilization Device With Advanced Deep Learning And Automatic Functionality

VOLUME-11; ISSUE-5; MAY-2025

Author(s): 1.S. Pavithra, 2. G. Dhinesh, 3.R. Mithuna

Authors Affiliations:

1.Ms. S. PAVITHRA, Assistant Professor, Department of Biomedical Engineering, Sri Shakthi Institute of Engineering & Technology, Coimbatore, Tamil Nadu.

2.Mr. G. DHINESH, Student, Department of Biomedical Engineering, Sri Shakthi Institute of Engineering & Technology, Coimbatore, Tamil Nadu.

3.Ms. R. MITHUNA, Student, Department of Biomedical Engineering, Sri Shakthi Institute of Engineering & Technology, Coimbatore, Tamil Nadu.

DOIs:10.2015/IJIRMF/202505007     |     Paper ID: IJIRMF202505007


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Sterilization is a vital process in medical and industrial environments to eliminate harmful microorganisms and prevent contamination. This paper presents a smart UV-C sterilization system incorporating a deep learning approach to detect and classify contamination levels using image analysis. The system integrates a MobileNetV2-based Convolutional Neural Network (CNN) model with an ESP32-CAM module to analyze images of objects before sterilization. Based on the contamination detected, the UV-C LED sterilization process is dynamically controlled. Experimental results confirm that the system provides efficient, adaptive sterilization with low latency, making it suitable for healthcare, pharmaceutical, and food processing applications. This approach improves patient safety, minimizes manual oversight, and contributes to infection control.

UV-C sterilization, Convolutional Neural Network (CNN), MobileNetV2, ESP32-CAM, contamination detection, deep learning, healthcare safety.

S. Pavithra,  G. Dhinesh, R. Mithuna(2025); Smart Portable UV Sterilization Device With Advanced Deep Learning And Automatic Functionality, International Journal for Innovative Research in Multidisciplinary Field, ISSN(O): 2455-0620, Vol-11, Issue-5, Pp.40-44.          Available on –   https://www.ijirmf.com/

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Healthcare 2023, 11(23), 3096; https://doi.org/10.3390/healthcare11233096

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