Open Access

ARTICLE

Image Recognition Based on Deep Learning with Thermal Camera Sensing

Wen-Tsai Sung¹, Chin-Hsuan Lin¹, Sung-Jung Hsiao^2,*

1 Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung, 411030, Taiwan
2 Department of Information Technology, Takming University of Science and Technology, Taipei, 11451, Taiwan

* Corresponding Author: Sung-Jung Hsiao. Email:

Computer Systems Science and Engineering 2023, 46(1), 505-520. https://doi.org/10.32604/csse.2023.034781

Received 27 July 2022; Accepted 28 October 2022; Issue published 20 January 2023

Abstract

As the COVID-19 epidemic spread across the globe, people around the world were advised or mandated to wear masks in public places to prevent its spreading further. In some cases, not wearing a mask could result in a fine. To monitor mask wearing, and to prevent the spread of future epidemics, this study proposes an image recognition system consisting of a camera, an infrared thermal array sensor, and a convolutional neural network trained in mask recognition. The infrared sensor monitors body temperature and displays the results in real-time on a liquid crystal display screen. The proposed system reduces the inefficiency of traditional object detection by providing training data according to the specific needs of the user and by applying You Only Look Once Version 4 (YOLOv4) object detection technology, which experiments show has more efficient training parameters and a higher level of accuracy in object recognition. All datasets are uploaded to the cloud for storage using Google Colaboratory, saving human resources and achieving a high level of efficiency at a low cost.

---

Keywords

---