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10.1007/s11042-021-10711-8 http://scihub22266oqcxt.onion/10.1007/s11042-021-10711-8 33679209!7917166!33679209     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Multimed+Tools+Appl 2021 ; 80 (13): 19753-19768 Nephropedia Template TP {{tp|p=33679209|t=2021. Face mask detection using YOLOv3 and faster R-CNN models: COVID-19 environment |pdf=|usr=}}{{33679209}} gab.com Text Face mask detection using YOLOv3 and faster R-CNN models: COVID-19 environment JO: Multimed Tools Appl http://www.kidney.de/pget.php?&tw=1&pmid=33679209 #FOAvip There are many solutions to prevent the spread of the COVID-19 virus and one of the most effective solutions is wearing a face mask. Almost everyone is wearing face masks at all times in public places during the coronavirus pandemic. This encourages us to explore face mask detection technology to monitor people wearing masks in public places. Most recent and advanced face mask detection approaches are designed using deep learning. In this article, two state-of-the-art object detection models, namely, YOLOv3 and faster R-CNN are used to achieve this task. The authors have trained both the models on a dataset that consists of images of people of two categories that are with and without face masks. This work proposes a technique that will draw bounding boxes (red or green) around the faces of people, based on whether a person is wearing a mask or not, and keeps the record of the ratio of people wearing face masks on the daily basis. The authors have also compared the performance of both the models i.e., their precision rate and inference time. Twit Text FOAVip Face mask detection using YOLOv3 and faster R-CNN models: COVID-19 environment ????Multimed Tools Appl http://www.kidney.de/pget.php?&tw=1&pmid=33679209 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33679209 #FOAvip English Wikipedia <ref>{{Cite pmid|33679209}}</ref> Face mask detection using YOLOv3 and faster R-CNN models: COVID-19 environment #MMPMID33679209 Singh S; Ahuja U; Kumar M; Kumar K; Sachdeva M Multimed Tools Appl 2021[]; 80 (13): 19753-19768 PMID33679209show ga There are many solutions to prevent the spread of the COVID-19 virus and one of the most effective solutions is wearing a face mask. Almost everyone is wearing face masks at all times in public places during the coronavirus pandemic. This encourages us to explore face mask detection technology to monitor people wearing masks in public places. Most recent and advanced face mask detection approaches are designed using deep learning. In this article, two state-of-the-art object detection models, namely, YOLOv3 and faster R-CNN are used to achieve this task. The authors have trained both the models on a dataset that consists of images of people of two categories that are with and without face masks. This work proposes a technique that will draw bounding boxes (red or green) around the faces of people, based on whether a person is wearing a mask or not, and keeps the record of the ratio of people wearing face masks on the daily basis. The authors have also compared the performance of both the models i.e., their precision rate and inference time. äFace mask detection using YOLOv3 and faster R-CNN models: COVID-19 env(epmc).pdf DeepDyve Pubget Overpricing