A Practical Hybrid IoT Architecture with Deep Learning Technique for Healthcare and Security Applications
Abstract
:1. Introduction
- YOLO tiny V4 was used to measure the accuracy of a masked face using a custom-built dataset of a blend of several data sets.
- Mask detection algorithms that provide high accuracy and frame rates were analyzed, so that the system can operate in real time.
- The use of MQTT communication for IoT applications allows the connection of devices to servers and data storage to become simpler and more convenient.
2. Methodology
2.1. System Overview
- (1)
- Device-side: The intelligent camera is powered by NVIDIA Jetson Nano. Based on an optimized deep learning detection model, NVIDIA Jetson Nano, which is considered the brain of the intelligent camera, captures and detects mask-wearing or mask-no-wearing cases. Device-side can contain a variety of devices installed in different surveillance locations.
- (2)
- Server-side: Data received from the device side are stored on the server, which is regarded as a warehouse. Data detected are then presented on a dashboard for analysis. It also handles the user’s commands and feedback and then transmits them to the device.
2.2. Hardware Installation
2.3. Software Development
2.3.1. Deep Mask Detection Model and Optimization
2.3.2. MQTT Broker and Webserver
- (1)
- Device selection: The device is selected to view its recorded data.
- (2)
- Filters: By selecting the date/time, the data will be visualized.
- (3)
- Reported statistics: Analyzing and visualizing the statistical data gathered from the selected device during a specific period.
3. Results and Discussions
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Model | YOLOv4-Full | YOLOv4-Tiny | MobileNetV2 |
---|---|---|---|
Input resolution | 608 × 608 | 1024 × 608 | 1024 × 608 |
FPS | 2.5 | 14 | 6 |
Accuracy (mAP) | 89.01% | 84.5% | 86.12% |
Precision = (1) | Accuracy = (2) |
Recall = (3) | F1_score = (4) |
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Vu, V.Q.; Tran, M.-Q.; Amer, M.; Khatiwada, M.; Ghoneim, S.S.M.; Elsisi, M. A Practical Hybrid IoT Architecture with Deep Learning Technique for Healthcare and Security Applications. Information 2023, 14, 379. https://doi.org/10.3390/info14070379
Vu VQ, Tran M-Q, Amer M, Khatiwada M, Ghoneim SSM, Elsisi M. A Practical Hybrid IoT Architecture with Deep Learning Technique for Healthcare and Security Applications. Information. 2023; 14(7):379. https://doi.org/10.3390/info14070379
Chicago/Turabian StyleVu, Viet Q., Minh-Quang Tran, Mohammed Amer, Mahesh Khatiwada, Sherif S. M. Ghoneim, and Mahmoud Elsisi. 2023. "A Practical Hybrid IoT Architecture with Deep Learning Technique for Healthcare and Security Applications" Information 14, no. 7: 379. https://doi.org/10.3390/info14070379
APA StyleVu, V. Q., Tran, M. -Q., Amer, M., Khatiwada, M., Ghoneim, S. S. M., & Elsisi, M. (2023). A Practical Hybrid IoT Architecture with Deep Learning Technique for Healthcare and Security Applications. Information, 14(7), 379. https://doi.org/10.3390/info14070379