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10.3390/s21093263 http://scihub22266oqcxt.onion/10.3390/s21093263 34066802!8125872!34066802     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       Sensors+(Basel) 2021 ; 21 (9): ä Nephropedia Template TP {{tp|p=34066802|t=2021. Face Mask Wearing Detection Algorithm Based on Improved YOLO-v4 |pdf=|usr=}}{{34066802}} gab.com Text Face Mask Wearing Detection Algorithm Based on Improved YOLO-v4 JO: Sensors (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=34066802 #FOAvip To solve the problems of low accuracy, low real-time performance, poor robustness and others caused by the complex environment, this paper proposes a face mask recognition and standard wear detection algorithm based on the improved YOLO-v4. Firstly, an improved CSPDarkNet53 is introduced into the trunk feature extraction network, which reduces the computing cost of the network and improves the learning ability of the model. Secondly, the adaptive image scaling algorithm can reduce computation and redundancy effectively. Thirdly, the improved PANet structure is introduced so that the network has more semantic information in the feature layer. At last, a face mask detection data set is made according to the standard wearing of masks. Based on the object detection algorithm of deep learning, a variety of evaluation indexes are compared to evaluate the effectiveness of the model. The results of the comparations show that the mAP of face mask recognition can reach 98.3% and the frame rate is high at 54.57 FPS, which are more accurate compared with the exiting algorithm. Twit Text FOAVip Face Mask Wearing Detection Algorithm Based on Improved YOLO-v4 ????Sensors (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=34066802 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34066802 #FOAvip English Wikipedia <ref>{{Cite pmid|34066802}}</ref> Face Mask Wearing Detection Algorithm Based on Improved YOLO-v4 #MMPMID34066802 Yu J; Zhang W Sensors (Basel) 2021[May]; 21 (9): ä PMID34066802show ga To solve the problems of low accuracy, low real-time performance, poor robustness and others caused by the complex environment, this paper proposes a face mask recognition and standard wear detection algorithm based on the improved YOLO-v4. Firstly, an improved CSPDarkNet53 is introduced into the trunk feature extraction network, which reduces the computing cost of the network and improves the learning ability of the model. Secondly, the adaptive image scaling algorithm can reduce computation and redundancy effectively. Thirdly, the improved PANet structure is introduced so that the network has more semantic information in the feature layer. At last, a face mask detection data set is made according to the standard wearing of masks. Based on the object detection algorithm of deep learning, a variety of evaluation indexes are compared to evaluate the effectiveness of the model. The results of the comparations show that the mAP of face mask recognition can reach 98.3% and the frame rate is high at 54.57 FPS, which are more accurate compared with the exiting algorithm. |*COVID-19[MESH] |*Facial Recognition[MESH] |Algorithms[MESH] |Humans[MESH] |Masks[MESH]Face Mask Wearing Detection Algorithm Based on Improved YOLO-v4 (epmc).pdf DeepDyve Pubget Overpricing