Dubey Parul, Keswani Vinay, Dubey Pushkar, Keswani Gunjan, Bhagat Dhananjay
Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune, India.
Department of Electronics and Telecommunication Engineering, G H Raisoni College of Engineering, Nagpur, India.
MethodsX. 2025 Mar 29;14:103291. doi: 10.1016/j.mex.2025.103291. eCollection 2025 Jun.
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.•The article introduces a novel hybrid framework that combines ResNet50 and MobileNetV2 architectures optimized with Adaptive Flame-Sailfish Optimization (AFSO).•The system demonstrates superior performance, achieving 97.8 % accuracy on the Kaggle dataset, with improved efficiency for IoT-based real-time applications.•Validates the framework's robustness and scalability across diverse datasets, addressing computational and environmental challenges.
物联网与深度学习的融合彻底改变了实时监测系统，尤其是在诸如口罩检测等公共卫生应用领域。随着公众对这些技术的依赖日益增加，强大且高效的框架对于确保遵守健康措施至关重要。另一方面，现有模型往往存在问题，比如计算速度慢、无法在广泛的环境中良好运行，以及无法很好地适应资源有限的物联网设备。这些缺点凸显了对优化且可扩展解决方案的需求。为解决这些问题，本研究使用了三个数据集：Kaggle口罩数据集、公共场所数据集和公共视频数据集，涵盖了不同的环境条件和用例。所提出的框架集成了ResNet50和MobileNetV2架构，并使用自适应火焰旗鱼优化（AFSO）算法进行了优化。这种混合方法提高了检测精度和计算效率，使其适用于实时部署。本文的新颖之处在于将AFSO与混合深度学习架构相结合以进行参数优化并提高可扩展性。使用诸如准确率、灵敏度、精确率和F1分数等性能指标来评估模型。所提出的框架在Kaggle数据集上达到了97.8%的准确率，显著优于基线模型，并证明了其在支持物联网的口罩检测系统中的鲁棒性和效率。
•本文介绍了一种新颖的混合框架，该框架结合了通过自适应火焰旗鱼优化（AFSO）优化的ResNet50和MobileNetV2架构。
•该系统展示了卓越的性能，在Kaggle数据集上达到了97.8%的准确率，提高了基于物联网的实时应用的效率。
•验证了该框架在不同数据集上的鲁棒性和可扩展性，解决了计算和环境方面的挑战。
