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HRNET: AI-on-Edge for Mask Detection and Social Distancing Calculation

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Abstract

The purpose of the paper is to provide innovative emerging technology framework for community to combat epidemic situations. The paper proposes a unique outbreak response system framework based on artificial intelligence and edge computing for citizen centric services to help track and trace people eluding safety policies like mask detection and social distancing measure in public or workplace setup. The framework further provides implementation guideline in industrial setup as well for governance and contact tracing tasks. The adoption will thus lead in smart city planning and development focusing on citizen health systems contributing to improved quality of life. The conceptual framework presented is validated through quantitative data analysis via secondary data collection from researcher’s public websites, GitHub repositories and renowned journals and further benchmarking were conducted for experimental results in Microsoft Azure cloud environment. The study includes selective AI models for benchmark analysis and were assessed on performance and accuracy in edge computing environment for large-scale societal setup. Overall YOLO model outperforms in object detection task and is faster enough for mask detection and HRNetV2 outperform semantic segmentation problem applied to solve social distancing task in AI-Edge inferencing environmental setup. The paper proposes new Edge-AI algorithm for building technology-oriented solutions for detecting mask in human movement and social distance. The paper enriches the technological advancement in artificial intelligence and edge computing applied to problems in society and healthcare systems. The framework further equips government agency, system providers to design and construct technology-oriented models in community setup to increase the quality of life using emerging technologies into smart urban environments.

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Data Availability

The datasets generated during and/or analyzed during the current study are available in the relevant academic repository hosted by Oxford and Standford, https://exposing.ai/oxford_town_centre/ and https://cs.stanford.edu/~roozbeh/pascal-context/.

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Authors and Affiliations

  1. Microsoft Corporation, New Delhi, India

    Kinshuk Sengupta

  2. Indian Institute of Management, Management, City Southern Bypass, Sunaria, Rohtak, Haryana, 124010, India

    Praveen Ranjan Srivastava

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  1. Kinshuk Sengupta
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Correspondence to Kinshuk Sengupta.

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This article is part of the topical collection “Advances in Computational Approaches for Artificial Intelligence, Image Processing, IoT and Cloud Applications” guest edited by Bhanu Prakash K. N. and M. Shivakumar.

Appendix 1: Terminology Classification

Appendix 1: Terminology Classification

CNN: convolutional neural network

FPGA: field programmable gate array

R-CNN: regions with CNN features

YOLO: you only look once; an object detection system trained on COCO dataset

HRNet: high-resolution networks

mPA: mean average precision

COCO: common objects in context

FPS: frames per second

GPU: graphical processing unit

TP/TN: true positive/true negative

SGD: stochastic gradient descent

DL: deep learning

IoT: internet of things

PASCAL: pattern analysis statistical modeling and computational learning

FPS: frames per second

VOC: visual object classes

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Sengupta, K., Srivastava, P.R. HRNET: AI-on-Edge for Mask Detection and Social Distancing Calculation. SN COMPUT. SCI. 3, 157 (2022). https://doi.org/10.1007/s42979-022-01023-1

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