R. N. S. V. Yalamarthi, Shareef Shaik, Dalwinder Singh, Manik Rakhra
{"title":"Real-Time Face Mask Detection Using Streamlit,TensorFlow, Keras and Open-CV","authors":"R. N. S. V. Yalamarthi, Shareef Shaik, Dalwinder Singh, Manik Rakhra","doi":"10.1109/ICDSIS55133.2022.9915817","DOIUrl":null,"url":null,"abstract":"This paper is based on the protection of our health from coronavirus officially known as COVID-19. Real-time detection of a face mask can help to prevent of the coronavirus, detecting the mask with the help of machine learning and data science algorithms such as Streamlit, MoblieNetV2, OpenCV, etc., are widely used in this ideal methodology. This paper is about the method that provides an accuracy of 99.78% in detecting the mask with live video stream. The method proposes building accurate model and integrating the model with a graphical interface which can improve the experience of the user.","PeriodicalId":178360,"journal":{"name":"2022 IEEE International Conference on Data Science and Information System (ICDSIS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Conference on Data Science and Information System (ICDSIS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDSIS55133.2022.9915817","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
This paper is based on the protection of our health from coronavirus officially known as COVID-19. Real-time detection of a face mask can help to prevent of the coronavirus, detecting the mask with the help of machine learning and data science algorithms such as Streamlit, MoblieNetV2, OpenCV, etc., are widely used in this ideal methodology. This paper is about the method that provides an accuracy of 99.78% in detecting the mask with live video stream. The method proposes building accurate model and integrating the model with a graphical interface which can improve the experience of the user.