Deep Learning-Based Mask Identification System Using ResNet Transfer Learning Architecture  

作　　者：Arpit Jain Nageswara Rao Moparthi A.Swathi Yogesh Kumar Sharma Nitin Mittal Ahmed Alhussen Zamil S.Alzamil MohdAnul Haq 

机构地区：[1]Department of Computer Science and Engineering,KoneruLakshmaiah Education Foundation,Vaddeswaram,522302,India [2]Department of Computer Science and Engineering,Sreyas Institute of Engineering and Technology,Hyderabad,500068,India [3]University Centre for Research and Development,Chandigarh University,Mohali,140413,India [4]Department of Computer Engineering,College of Computer and Information Sciences,Majmaah University,Al-Majmaah,11952,Saudi Arabia [5]Department of Computer Science,College of Computer and Information Sciences,Majmaah University,Al-Majmaah,11952,Saudi Arabia

出　　处：《Computer Systems Science & Engineering》2024年第2期341-362,共22页计算机系统科学与工程（英文）

基　　金：This work was supported by Deanship of Scientific Research at Majmaah University under Project No.R-2023-356.

摘　　要：Recently,the coronavirus disease 2019 has shown excellent attention in the global community regarding health and the economy.World Health Organization(WHO)and many others advised controlling Corona Virus Disease in 2019.The limited treatment resources,medical resources,and unawareness of immunity is an essential horizon to unfold.Among all resources,wearing a mask is the primary non-pharmaceutical intervention to stop the spreading of the virus caused by Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2)droplets.All countries made masks mandatory to prevent infection.For such enforcement,automatic and effective face detection systems are crucial.This study presents a face mask identification approach for static photos and real-time movies that distinguishes between images with and without masks.To contribute to society,we worked on mask detection of an individual to adhere to the rule and provide awareness to the public or organization.The paper aims to get detection accuracy using transfer learning from Residual Neural Network 50(ResNet-50)architecture and works on detection localization.The experiment is tested with other popular pre-trained models such as Deep Convolutional Neural Networks(AlexNet),Residual Neural Networks(ResNet),and Visual Geometry Group Networks(VGG-Net)advanced architecture.The proposed system generates an accuracy of 98.4%when modeled using Residual Neural Network 50(ResNet-50).Also,the precision and recall values are proved as better when compared to the existing models.This outstanding work also can be used in video surveillance applications.

关 键 词：Transfer learning depth analysis convolutional neural networks(CNN) COVID-19 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]