An Efficient and Effective Deep Learning-Based Model for Real-Time Face Mask Detection
Since December 2019, the COVID-19 pandemic has led to a dramatic loss of human lives and caused severe economic crises worldwide. COVID-19 virus transmission generally occurs through a small respiratory droplet ejected from the mouth or nose of an infected person to another person. To reduce and pre...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/7/2602 |
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| author | Shabana Habib Majed Alsanea Mohammed Aloraini Hazim Saleh Al-Rawashdeh Muhammad Islam Sheroz Khan |
| author_facet | Shabana Habib Majed Alsanea Mohammed Aloraini Hazim Saleh Al-Rawashdeh Muhammad Islam Sheroz Khan |
| author_sort | Shabana Habib |
| collection | DOAJ |
| description | Since December 2019, the COVID-19 pandemic has led to a dramatic loss of human lives and caused severe economic crises worldwide. COVID-19 virus transmission generally occurs through a small respiratory droplet ejected from the mouth or nose of an infected person to another person. To reduce and prevent the spread of COVID-19 transmission, the World Health Organization (WHO) advises the public to wear face masks as one of the most practical and effective prevention methods. Early face mask detection is very important to prevent the spread of COVID-19. For this purpose, we investigate several deep learning-based architectures such as VGG16, VGG19, InceptionV3, ResNet-101, ResNet-50, EfficientNet, MobileNetV1, and MobileNetV2. After these experiments, we propose an efficient and effective model for face mask detection with the potential to be deployable over edge devices. Our proposed model is based on MobileNetV2 architecture that extracts salient features from the input data that are then passed to an autoencoder to form more abstract representations prior to the classification layer. The proposed model also adopts extensive data augmentation techniques (e.g., rotation, flip, Gaussian blur, sharping, emboss, skew, and shear) to increase the number of samples for effective training. The performance of our proposed model is evaluated on three publicly available datasets and achieved the highest performance as compared to other state-of-the-art models. |
| first_indexed | 2024-03-09T11:26:39Z |
| format | Article |
| id | doaj.art-7d25ce9717bb45e9b3532187861c872f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T11:26:39Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-7d25ce9717bb45e9b3532187861c872f2023-12-01T00:01:47ZengMDPI AGSensors1424-82202022-03-01227260210.3390/s22072602An Efficient and Effective Deep Learning-Based Model for Real-Time Face Mask DetectionShabana Habib0Majed Alsanea1Mohammed Aloraini2Hazim Saleh Al-Rawashdeh3Muhammad Islam4Sheroz Khan5Department of Information Technology, College of Computer, Qassim University, Buraydah 52571, Saudi ArabiaComputing Department, Arabeast Colleges, Riyadh 13544, Saudi ArabiaDepartment of Electrical Engineering, College of Engineering, Qassim University, Qassim 52571, Saudi ArabiaCyber Security Department, College of Engineering and Information Technology, Onaizah Colleges, Onaizah 56447, Saudi ArabiaDepartment of Electrical Engineering, College of Engineering and Information Technology, Onaizah Colleges, Onaizah 56447, Saudi ArabiaDepartment of Electrical Engineering, College of Engineering and Information Technology, Onaizah Colleges, Onaizah 56447, Saudi ArabiaSince December 2019, the COVID-19 pandemic has led to a dramatic loss of human lives and caused severe economic crises worldwide. COVID-19 virus transmission generally occurs through a small respiratory droplet ejected from the mouth or nose of an infected person to another person. To reduce and prevent the spread of COVID-19 transmission, the World Health Organization (WHO) advises the public to wear face masks as one of the most practical and effective prevention methods. Early face mask detection is very important to prevent the spread of COVID-19. For this purpose, we investigate several deep learning-based architectures such as VGG16, VGG19, InceptionV3, ResNet-101, ResNet-50, EfficientNet, MobileNetV1, and MobileNetV2. After these experiments, we propose an efficient and effective model for face mask detection with the potential to be deployable over edge devices. Our proposed model is based on MobileNetV2 architecture that extracts salient features from the input data that are then passed to an autoencoder to form more abstract representations prior to the classification layer. The proposed model also adopts extensive data augmentation techniques (e.g., rotation, flip, Gaussian blur, sharping, emboss, skew, and shear) to increase the number of samples for effective training. The performance of our proposed model is evaluated on three publicly available datasets and achieved the highest performance as compared to other state-of-the-art models.https://www.mdpi.com/1424-8220/22/7/2602COVID-19convolution neural networkdata augmentationdeep learningface maskmachine learning |
| spellingShingle | Shabana Habib Majed Alsanea Mohammed Aloraini Hazim Saleh Al-Rawashdeh Muhammad Islam Sheroz Khan An Efficient and Effective Deep Learning-Based Model for Real-Time Face Mask Detection Sensors COVID-19 convolution neural network data augmentation deep learning face mask machine learning |
| title | An Efficient and Effective Deep Learning-Based Model for Real-Time Face Mask Detection |
| title_full | An Efficient and Effective Deep Learning-Based Model for Real-Time Face Mask Detection |
| title_fullStr | An Efficient and Effective Deep Learning-Based Model for Real-Time Face Mask Detection |
| title_full_unstemmed | An Efficient and Effective Deep Learning-Based Model for Real-Time Face Mask Detection |
| title_short | An Efficient and Effective Deep Learning-Based Model for Real-Time Face Mask Detection |
| title_sort | efficient and effective deep learning based model for real time face mask detection |
| topic | COVID-19 convolution neural network data augmentation deep learning face mask machine learning |
| url | https://www.mdpi.com/1424-8220/22/7/2602 |
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