A Cloud-Edge-Terminal Collaborative System for Temperature Measurement in COVID-19 Prevention

TL;DR

This paper introduces a real-time, safe, and accurate temperature measurement system using cloud-edge-terminal collaboration, thermal imaging, and face/mask detection to aid COVID-19 prevention in public spaces.

Contribution

It presents a lightweight, multi-task detection model and a novel temperature measurement method that improves safety and accuracy over existing approaches.

Findings

Detection model size is 6.1MB and runs at 257ms.

Indoor temperature measurement error is about 3% at 1m distance.

System enables real-time temperature monitoring in public areas.

Abstract

To prevent the spread of coronavirus disease 2019 (COVID-19), preliminary temperature measurement and mask detection in public areas are conducted. However, the existing temperature measurement methods face the problems of safety and deployment. In this paper, to realize safe and accurate temperature measurement even when a person's face is partially obscured, we propose a cloud-edge-terminal collaborative system with a lightweight infrared temperature measurement model. A binocular camera with an RGB lens and a thermal lens is utilized to simultaneously capture image pairs. Then, a mobile detection model based on a multi-task cascaded convolutional network (MTCNN) is proposed to realize face alignment and mask detection on the RGB images. For accurate temperature measurement, we transform the facial landmarks on the RGB images to the thermal images by an affine transformation and select a more accurate temperature measurement area on the forehead. The collected information is uploaded to the cloud in real time for COVID-19 prevention. Experiments show that the detection model is only 6.1M and the average detection speed is 257ms. At a distance of 1m, the error of indoor temperature measurement is about 3%. That is, the proposed system can realize real-time temperature measurement in public areas.