Detecting Appropriate and Inappropriate COVID-19 Face Mask Wear in Controlled Environments Using Transfer Learning-Based Convolutional Neural Network

The most recent epidemic of coronavirus disease (COVID-19) was brought on by a coronavirus subsequently found. The general population was still required to wear surgical face masks, sometimes known as medical masks, to safeguard against the coronavirus disease and monkeypox disease as well brought on by COVID-19 and monkeypox virus. In the majority of regulated conditions, it might be challenging to see if someone is wearing their mask properly. The researchers imply a COVID-19 detection of correct and improper wearing of surgical face masks in regulated areas as a way to help with the ongoing development of identification of facemask wearing to limit the spread of the virus. Models generated using deep learning to identify persons’ proper wearing of masks were used. The model with the lowest performance in this study’s model evaluation, Model 3, has an mAP of 0.0777. With an mAP of 0.9668 (96.68%) and 3.31 training loss, the model produced the best results in model 42. The said model obtained the highest mAP, which was used for testing and inferencing as a result. This study showed promising results and might be used to reliably identify appropriate mask wear in public by using proper detection of facemask technology.