Towards an Approach for Filtration Efficiency Estimation of Consumer-Grade Face Masks Using Thermography

Abstract

Due to the increasing need for continuous use of face masks caused by COVID-19, it is essential to evaluate the filtration quality that each face mask provides. In this research, an estimation method based on thermal image processing was developed; the main objective was to evaluate the effectiveness of different face masks while being used during breathing. For the acquisition of heat distribution images, a thermographic imaging system was built; moreover, a deep learning model detected the leakage percentage of each face mask with a mAP of 0.9345, recall of 0.842 and F1-score of 0.82. The results obtained from this research revealed that the filtration effectiveness depended on heat loss through the manufacturing material; the proposed estimation method is simple, fast, and can be replicated and operated by people who are not experts in the computer field.

A critical review on the role of leakages in the facemask protection against SARS‐CoV‐2 infection with consideration of vaccination and virus variants

“…However, the fast cooling of the exhaled flow limits the visualization to the immediate vicinity of the wearer's head. A more recent work combined a deep learning model with infrared thermography 79 for a faster and automated detection of leaking airflows by comparing the temperature information with and without masks. Such a method would allow a more precise detection of the leaking flow compared with the unassisted estimation of the leaking flow from thermal mapping; but remains sensitive to ambient temperature and humidity.…”