Quick Mask Detection with Edge Detection model and YOLO۵ Detector in Deep Learning

In this century, without a doubt, if we ask different people in society, about one of the ۳ mostimportant events in the life of humanity in the last ۵ years, everyone will point to the spread ofthe Coronavirus as one of the most bitter and of course the most influential events in their lives Atype of viral disease with a high level of transmission and widespread in the world, which has lefthuge human casualties. Undoubtedly, according to the published statistics, it is time for computerscience and especially artificial intelligence to once again enter the campaign against the coronaepidemic to help human society in the context of making it smarter. Currently, deep learningalong with machine vision has provided a suitable platform for solving problems such as maskrecognition.Background and aims: As you know, humans transmit the virus to their own species by talking,breathing and actually communicating, and we are all aware of the effect of prevention overtreatment. Apart from the treatment methods suggested in the medical field, both medicinal andnon-medicinal, the most effective and least expensive behavior and at the same time the mostapplicable prevention method is using a mask during communication. According to statistics, if itis used correctly, it prevents the transmission of the virus by ۸۰%.Method: The proposed algorithm with the help of edge detection and YOLO۵ object detector inthe context of artificial neural network and machine learning concepts has provided the possibilityof high-speed identification. It is better to know that (CNN) networks are used for identificationin this model.Results: Also, in the method, two performance evaluation criteria of YOLO۵, i.e. precision andaverage accuracy of (mAP_۰.۵) threshold have shown good results in ۱۰۰ training courses.Conclusion: Undoubtedly, artificial intelligence and artificial neural networks have taken a newstep towards protecting human health by providing different mask detection methods in the pastyears, but after the coronavirus epidemic the need for more accurate detection with less timeand costs is more necessary than past. It is hoped that in this research, by improving the powerof detection by presenting a new algorithm in the field of artificial intelligence, we have taken avaluable step toward the health of society.