A Cloud-Edge-Terminal Collaborative System for Temperature Measurement in COVID-19 Prevention
This addresses safety and deployment issues in temperature measurement for public health during COVID-19, but it is incremental as it builds on existing methods like MTCNN.
The paper tackles the problem of safe and accurate temperature measurement for COVID-19 prevention by proposing a cloud-edge-terminal collaborative system that uses a lightweight infrared model and binocular camera, achieving a 3% error in indoor temperature measurement at 1m distance and real-time detection with 257ms average speed.
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.