Innovative IoT-enabled mask detection system: A hybrid deep learning approach for public health applications

Abstract

The integration of IoT and deep learning has revolutionized real-time monitoring systems, particularly in public health applications such as face mask detection. With increasing public reliance on these technologies, robust and efficient frameworks are critical for ensuring compliance with health measures. Existing models, on the other hand, often have problems, such as being slow to compute, not being able to work well in a wide range of environments, and not being able to adapt well to IoT devices with limited resources. These shortcomings highlight the need for an optimized and scalable solution. To address these issues, this study utilizes three datasets: the Kaggle Face Mask Dataset, the Public Places Dataset, and the Public Videos Dataset, encompassing varied environmental conditions and use cases. The proposed framework integrates ResNet50 and MobileNetV2 architectures, optimized using the Adaptive Flame-Sailfish Optimization (AFSO) algorithm. This hybrid approach enhances detection accuracy and computational efficiency, making it suitable for real-time deployment. The novelty of the paper lies in combining AFSO with a hybrid deep learning architecture for parameter optimization and improved scalability. Performance metrics such as accuracy, sensitivity, precision, and F1-score were used to evaluate the model. The proposed framework achieved an accuracy of 97.8 % on the Kaggle dataset, significantly outperforming baseline models and demonstrating its robustness and efficiency for IoT-enabled face mask detection systems.

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  The article introduces a novel hybrid framework that combines ResNet50 and MobileNetV2 architectures optimized with Adaptive Flame-Sailfish Optimization (AFSO).
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  The system demonstrates superior performance, achieving 97.8 % accuracy on the Kaggle dataset, with improved efficiency for IoT-based real-time applications.
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  Validates the framework's robustness and scalability across diverse datasets, addressing computational and environmental challenges.