Enhanced IoT-Based Face Mask Detection Framework Using Optimized Deep Learning Models: A Hybrid Approach With Adaptive Algorithms
The COVID-19 pandemic has made face mask detection into a big thing because it is essential in public health monitoring. Meanwhile, the growing number of things that can be connected to the internet and the increasing integration of this technology mean that edge devices are now in demand for effect...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10849539/ |
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| author | Parul Dubey Pushkar Dubey Celestine Iwendi Cresantus N. Biamba Deepak Dasaratha Rao |
| author_facet | Parul Dubey Pushkar Dubey Celestine Iwendi Cresantus N. Biamba Deepak Dasaratha Rao |
| author_sort | Parul Dubey |
| collection | DOAJ |
| description | The COVID-19 pandemic has made face mask detection into a big thing because it is essential in public health monitoring. Meanwhile, the growing number of things that can be connected to the internet and the increasing integration of this technology mean that edge devices are now in demand for effective real-time face mask detection models. Often, existing methods require some kind of pre-installed equipment or difficult-to-manipulate environmental conditions, and computational resource constraints essentially put an end to them. In the present study, a hybrid Flame-Sailfish Optimization (HFSO)-based deep learning framework is proposed. It combines the feature extraction capabilities of ResNet50 with the efficiency of MobileNetV2. The HFSO algorithm optimizes crucial parameters such as detection thresholds and learning rates. So that the model can take full advantage of computing capacity and still operate in real time on devices with limited resources. The model was tested on three data sets—Kaggle Face Mask Detection dataset, Public Places dataset, and Public Videos dataset—achieving up to 97.5% accuracy. It outperformed the previous leader in all cases. The results prove that this framework is reliable and easily applicable for identifying people wearing masks under different conditions. However, where there is great occlusion of the face or video feed quality is bad, the model’s performance will drop somewhat. Future work should focus on increasing difficulty in detections, broadening the application of this method to other health monitoring systems based on the Internet of Things, and ensuring that its robustness remains unaltered. |
| format | Article |
| id | doaj-art-c567fc9564514872a9d5e819e26aa56e |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
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| spelling | doaj-art-c567fc9564514872a9d5e819e26aa56e2025-01-31T00:00:53ZengIEEEIEEE Access2169-35362025-01-0113173251733910.1109/ACCESS.2025.353276410849539Enhanced IoT-Based Face Mask Detection Framework Using Optimized Deep Learning Models: A Hybrid Approach With Adaptive AlgorithmsParul Dubey0https://orcid.org/0000-0001-8903-6664Pushkar Dubey1Celestine Iwendi2Cresantus N. Biamba3https://orcid.org/0000-0002-0589-7924Deepak Dasaratha Rao4Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune, IndiaDepartment of Management, Pandit Sundarlal Sharma (Open) University, Bilaspur, Chhattisgarh, IndiaCentre of Intelligence of Things, School of Creative Technologies, University of Bolton, Bolton, U.K.Department of Educational Sciences, University of Gävle, Gävle, SwedenIndependent Researcher, Plano, TX, USAThe COVID-19 pandemic has made face mask detection into a big thing because it is essential in public health monitoring. Meanwhile, the growing number of things that can be connected to the internet and the increasing integration of this technology mean that edge devices are now in demand for effective real-time face mask detection models. Often, existing methods require some kind of pre-installed equipment or difficult-to-manipulate environmental conditions, and computational resource constraints essentially put an end to them. In the present study, a hybrid Flame-Sailfish Optimization (HFSO)-based deep learning framework is proposed. It combines the feature extraction capabilities of ResNet50 with the efficiency of MobileNetV2. The HFSO algorithm optimizes crucial parameters such as detection thresholds and learning rates. So that the model can take full advantage of computing capacity and still operate in real time on devices with limited resources. The model was tested on three data sets—Kaggle Face Mask Detection dataset, Public Places dataset, and Public Videos dataset—achieving up to 97.5% accuracy. It outperformed the previous leader in all cases. The results prove that this framework is reliable and easily applicable for identifying people wearing masks under different conditions. However, where there is great occlusion of the face or video feed quality is bad, the model’s performance will drop somewhat. Future work should focus on increasing difficulty in detections, broadening the application of this method to other health monitoring systems based on the Internet of Things, and ensuring that its robustness remains unaltered.https://ieeexplore.ieee.org/document/10849539/Hybrid flame-sailfish optimizationface mask detectiondeep learningIoT-enabled devicesResNet50 |
| spellingShingle | Parul Dubey Pushkar Dubey Celestine Iwendi Cresantus N. Biamba Deepak Dasaratha Rao Enhanced IoT-Based Face Mask Detection Framework Using Optimized Deep Learning Models: A Hybrid Approach With Adaptive Algorithms IEEE Access Hybrid flame-sailfish optimization face mask detection deep learning IoT-enabled devices ResNet50 |
| title | Enhanced IoT-Based Face Mask Detection Framework Using Optimized Deep Learning Models: A Hybrid Approach With Adaptive Algorithms |
| title_full | Enhanced IoT-Based Face Mask Detection Framework Using Optimized Deep Learning Models: A Hybrid Approach With Adaptive Algorithms |
| title_fullStr | Enhanced IoT-Based Face Mask Detection Framework Using Optimized Deep Learning Models: A Hybrid Approach With Adaptive Algorithms |
| title_full_unstemmed | Enhanced IoT-Based Face Mask Detection Framework Using Optimized Deep Learning Models: A Hybrid Approach With Adaptive Algorithms |
| title_short | Enhanced IoT-Based Face Mask Detection Framework Using Optimized Deep Learning Models: A Hybrid Approach With Adaptive Algorithms |
| title_sort | enhanced iot based face mask detection framework using optimized deep learning models a hybrid approach with adaptive algorithms |
| topic | Hybrid flame-sailfish optimization face mask detection deep learning IoT-enabled devices ResNet50 |
| url | https://ieeexplore.ieee.org/document/10849539/ |
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