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.
Highlights
Coronavirus disease 2019 (COVID-19) is a highly contagious and pathogenic viral infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), first reported in Wuhan, China, and currently widespread around the world [1,2]
Data obtained from the mean average precision (mAP) showed 1143 detections, and 311 unique truth values were taken into account
The results reveal that thermography represents significant advantages over other methods used to monitor the efficiency of facial masks since heat loss through the micropores is evaluated, and it represents the percentage of normal breathing that filters through the face masks, either when inhaling or exhaling
Summary
Coronavirus disease 2019 (COVID-19) is a highly contagious and pathogenic viral infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), first reported in Wuhan, China, and currently widespread around the world [1,2]. COVID-19 has been spread primarily from person to person through tiny droplets of fluid expelled from the nose or mouth of an infected person by coughing, sneezing, or talking [3]. Governments around the world have recommended the use of face masks to all their citizens [6], because face masks prevent the infection of COVID-19 between people [7]. Face masks’ effectiveness in preventing the spread of COVID-19 and other respiratory diseases has decreased mainly due to their misuse and poor fitting [8], including low-quality manufacturing processes and unsuitable materials [7]. Modern computer vision algorithms are approaching human-level performance in visual perception tasks
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