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10.1093/jamia/ocab052 http://scihub22266oqcxt.onion/10.1093/jamia/ocab052 33713140!7989332!33713140     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Am+Med+Inform+Assoc 2021 ; 28 (7): 1548-1554 Nephropedia Template TP {{tp|p=33713140|t=2021. Transfer learning for mobile real-time face mask detection and localization |pdf=|usr=}}{{33713140}} gab.com Text Transfer learning for mobile real-time face mask detection and localization JO: J Am Med Inform Assoc http://www.kidney.de/pget.php?&tw=1&pmid=33713140 #FOAvip OBJECTIVE: Due to the COVID-19 pandemic, our daily habits have suddenly changed. Gatherings are forbidden and, even when it is possible to leave the home for health or work reasons, it is necessary to wear a face mask to reduce the possibility of contagion. In this context, it is crucial to detect violations by people who do not wear a face mask. MATERIALS AND METHODS: For these reasons, in this article, we introduce a method aimed to automatically detect whether people are wearing a face mask. We design a transfer learning approach by exploiting the MobileNetV2 model to identify face mask violations in images/video streams. Moreover, the proposed approach is able to localize the area related to the face mask detection with relative probability. RESULTS: To asses the effectiveness of the proposed approach, we evaluate a dataset composed of 4095 images related to people wearing and not wearing face masks, obtaining an accuracy of 0.98 in face mask detection. DISCUSSION AND CONCLUSION: The experimental analysis shows that the proposed method can be successfully exploited for face mask violation detection. Moreover, we highlight that it is working also on device with limited computational capability and it is able to process in real time images and video streams, making our proposal applicable in the real world. Twit Text FOAVip Transfer learning for mobile real-time face mask detection and localization ????J Am Med Inform Assoc http://www.kidney.de/pget.php?&tw=1&pmid=33713140 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33713140 #FOAvip English Wikipedia <ref>{{Cite pmid|33713140}}</ref> Transfer learning for mobile real-time face mask detection and localization #MMPMID33713140 Mercaldo F; Santone A J Am Med Inform Assoc 2021[Jul]; 28 (7): 1548-1554 PMID33713140show ga OBJECTIVE: Due to the COVID-19 pandemic, our daily habits have suddenly changed. Gatherings are forbidden and, even when it is possible to leave the home for health or work reasons, it is necessary to wear a face mask to reduce the possibility of contagion. In this context, it is crucial to detect violations by people who do not wear a face mask. MATERIALS AND METHODS: For these reasons, in this article, we introduce a method aimed to automatically detect whether people are wearing a face mask. We design a transfer learning approach by exploiting the MobileNetV2 model to identify face mask violations in images/video streams. Moreover, the proposed approach is able to localize the area related to the face mask detection with relative probability. RESULTS: To asses the effectiveness of the proposed approach, we evaluate a dataset composed of 4095 images related to people wearing and not wearing face masks, obtaining an accuracy of 0.98 in face mask detection. DISCUSSION AND CONCLUSION: The experimental analysis shows that the proposed method can be successfully exploited for face mask violation detection. Moreover, we highlight that it is working also on device with limited computational capability and it is able to process in real time images and video streams, making our proposal applicable in the real world. |*Automated Facial Recognition[MESH] |*COVID-19[MESH] |*Deep Learning[MESH] |*Masks[MESH] |Datasets as Topic[MESH] |Female[MESH] |Humans[MESH]Transfer learning for mobile real-time face mask detection and localiz(epmc).pdf DeepDyve Pubget Overpricing