RESUMO
During an epidemic, accurate human temperature screening based on neural networks for disease surveillance is important and challenging. Existing distant human forehead temperature measuring device usually adopts a dual-camera system using paired RGB and thermal infrared images to conduct face detection and temperature measurement. Since the facial RGB image may undermine people's privacy, we designed a monocular thermal system and proposed an effective framework called the InfraNet to measure and calibrate forehead temperature of people in the wild. To address the challenge of temperature floating, the InfraNet calibrates the subject's temperature with one's physical depth and horizontal offset predicted by a single infrared image. Our InfraNet framework mainly consists of three parts: face detection subnet, depth and horizontal offset estimation subnet and temperature calibration subnet. The temperature calibration performance can be improved with the help of spatial regularization term concentrating on predicting precise depth and horizontal offset of people. Besides, we collected a large-scale infrared image dataset in the both lab and wild scenarios, including 8,215 thermal infrared images. Experiments on our wild dataset demonstrated that the InfraNet achieved 91.6% high accuracy of distant multi-subject temperature measurement on average under the standard temperature threshold of strict 0.3°C.