Indian Journal of Science and Technology
Year: 2021, Volume: 14, Issue: 38, Pages: 2899-2915
Premanand Ghadekar1*, Gurdeep Singh2, Joydeep Datta2, Aryan Kumar Gupta3, Divsehaj Singh Anand3, Shreyas Khare3, Preeti Oswal3,Dheeraj Sharma3
1Department of Information Technology, Vishwakarma Institute of Technology, Pune, 411037, India
2Corporate Venturing & Innovations Group, Tata Communications Ltd., Mumbai
3Department of Information Technology, Vishwakarma Institute of Technology, Pune, 411037, India
Email: [email protected]
Received Date:01 June 2021, Accepted Date:15 September 2021, Published Date:15 November 2021
Objectives: To propose a model which could classify in real-time if an individual is wearing a face mask or not wearing a face mask. A lightweight system that could be easily deployed and assist in surveillance. Methods/Statisticalanalysis: Analysis of the proposed model shows a limited number of research studies with regards to facial localizations. Several state-of-the-art methods were taken into considerations out of which the CNN architectural approach is analyzed in this study. Taking into consideration the use-case of deployments and structuring, a new Keras-based model is proposed that surpasses the achievement results of MobileNet-V2 and VGG-16 standard architectures. Effective facial localization is tackled with the MTCNN approach. Findings: The system has achieved a confidence score of 0.9914, an average weighted F1-score of 0.98, a precision value of 0.99. The proposed model has been compared with standard architectures of VGG16 and MobileNetV2 with regard to the accuracy, support values, precision, recall, and F1-score metrics. The proposed model performs better w.r.t traditional architectures. The average latency involved in prediction is 0.034 seconds making the average FPS 30 Frames per second. The compact architecture makes the model best for deployment in real-time scenarios. The system incorporates the concept of image localization with Multi-Task Cascaded Convolutional Neural Network (MTCNN) architecture. The analysis shows MTCNN is performing much better than Haar-Cascade in real-time facial prediction scenarios. Novelty/Applications: This compact architecture with minimal layers is easily deployable in edge devices. It can be used for mass screening at public places like railway stops, bus stops, streets, malls, entrances, schools, and many service-oriented business verticals requiring users to access the services as long as the mask has been worn correctly.
Keywords: COVID19; Deep learning; Computer Vision; Mask Detection; Deep Convolutional Neural Network; Image Localization
© 2021 Ghadekar et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)
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