An efficient face and mask detection by embedded device via thermal imaging camera

Abstract Since 87.9% of COVID-19 patients develop a fever symptom, the mandatory wearing of masks, temperature screening, and isolation of individuals with fever are the most import methods for community epidemic prevention. This study develops a thermal imaging thermometer embedded system with face, face mask, and temperature detection capabilities using low-end CPU devices. The hardware includes a Raspberry Pi 4, FLIR Lepton 3.5 LWIR Micro Thermal Camera Module, display screen, and alarm. The system is characterized by its low cost and lightweight design, and it only needs to detect necessary areas around the face area during face mask and temperature detection, effectively avoiding external interference and further enhancing the effectiveness of epidemic prevention. The face detection model is based on the YOLO-Fastest architecture and has been improved, referred to as Pi-fast. In terms of performance on the training and testing sets, our proposed Pi-fast achieves Mean Average Precision (mAP) values of 99.64% and 93.18%, F1 score values of 0.94 and 0.87, and Intersection over Union (IoU)values of 78.07% and 65.98%, respectively. Regarding system performance, the frame rate of FLIR Lepton 3.5 Thermal Camera Module hardware specifications is 8.7 FPS. When using YOLO-Fastest and proposed Pi-fast model for detection on the proposed thermal imaging system, the frame rates are 6.58 FPS and 7.32FPS, respectively. The proposed Pi-fast model outperforms YOLO-Fastest about 0.74 FPS (11.2%). The system can detect objects at a distance of about 7 meters from the lens in thermographic camera with a resolution of only 160×120 pixels, making it suitable for multi-person screening and improving the screening process’s efficiency. The system can effectively perform temperature screening and face mask detection, making it suitable for office buildings, schools, medical clinics, and other public areas. During the severe epidemic period, the proposed thermal imaging embedded system that was actually used for epidemic prevention work at I-Shou University, improving screening efficiency and reducing labour costs.