Journal article Open Access

Face Mask Detection in Real-Time using MobileNetv2

Mohamed Almghraby; Abdelrady Okasha Elnady

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    <subfield code="a">Coronavirus, Computer vision, Face Mask Detection, CoVid-19, MobileNetV2</subfield>
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    <subfield code="u">Abdelrady Okasha Elnady*, Head, Department of Mechatronics, Faculty of Engineering October 6 University, Egypt</subfield>
    <subfield code="a">Abdelrady Okasha Elnady</subfield>
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    <subfield code="a">Blue Eyes Intelligence Engineering  and Sciences Publication (BEIESP)</subfield>
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    <subfield code="l">open</subfield>
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    <subfield code="c">2021-08-30</subfield>
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    <subfield code="c">104-108</subfield>
    <subfield code="n">6</subfield>
    <subfield code="p">International Journal of Engineering and Advanced Technology (IJEAT)</subfield>
    <subfield code="v">10</subfield>
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    <subfield code="u">UG Student, Department of Mechatronics, Faculty of Engineering October 6 University, Egypt</subfield>
    <subfield code="a">Mohamed Almghraby</subfield>
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    <subfield code="a">Face Mask Detection in Real-Time using MobileNetv2</subfield>
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    <subfield code="a">Creative Commons Attribution 4.0 International</subfield>
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    <subfield code="0">(issn)2249-8958</subfield>
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    <subfield code="0">(handle)100.1/ijeat.F30500810621</subfield>
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    <subfield code="a">&lt;p&gt;Abstract: Face mask detection has made considerable progress in the field of computer vision since the start of the Covid-19 epidemic. Many efforts are being made to develop software that can detect whether or not someone is wearing a mask. Many methods and strategies have been used to construct face detection models. A created model for detecting face masks is described in this paper, which uses &amp;ldquo;deep learning&amp;rdquo;, &amp;ldquo;TensorFlow&amp;rdquo;, &amp;ldquo;Keras&amp;rdquo;, and &amp;ldquo;OpenCV&amp;rdquo;. The MobilenetV2 architecture is used as a foundation for the classifier to perform real-time mask identification. The present model dedicates 80 percent of the training dataset to training and 20% to testing, and splits the training dataset into 80% training and 20% validation, resulting in a final model with 65 percent of the dataset for training, 15 percent for validation, and 20% for testing. The optimization approach used in this experiment is &amp;ldquo;stochastic gradient descent&amp;rdquo; with momentum (&amp;ldquo;SGD&amp;rdquo;), with a learning rate of 0.001 and momentum of 0.85. The training and validation accuracy rose until they reached their maximal peak at epoch 12, with 99% training accuracy and 98% validation accuracy. The model&amp;#39;s training and validation losses both reduced until they reached their lowest at epoch 12, with a validation loss of 0.050% and a training loss of less than 0.025%. This system allows for real-time detection of someone is missing the appropriate face mask. This model is particularly resource-efficient when it comes to deployment, thus it can be employed for safety. So, this technique can be merged with embedded application systems at public places and public services places as airports, trains stations, workplaces, and schools to ensure subordination to the guidelines for public safety. The current version is compatible with both IP and non-IP cameras. Web and desktop apps can use the live video feed for detection. The program can also be linked to the entrance gates, allowing only those who are wearing masks to enter. It can also be used in shopping malls and universities.&amp;nbsp;&lt;/p&gt;</subfield>
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    <subfield code="a">10.35940/ijeat.F3050.0810621</subfield>
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