Design and Development of Modular and Multifunctional UAV with Amphibious Landing, Processing and Surround Sense Module

Kohli, Lakshit; Saurabh, Manglesh; Bhatia, Ishaan; Sindhwani, Nidhi; Vijh, Manjula

doi:10.1002/9781119769170.ch12

Cited by 12 publications

(1 citation statement)

References 21 publications

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“…Physical distancing, repeated hand sanitizing, and face masking have proven to be quite efficient to control the spreading of the virus, but everyone is not obeying the guidelines. Various technologies like machine learning (ML) algorithms [ 1 ], artificial intelligence (AI) approaches [ 1 ], Internet of things (IoT) [ 2 – 5 ], and unmanned aerial vehicles (UAV) [ 6 ] give a real-time scenario at any given point about (i) the number of people following physical distancing and (ii) whether people are wearing masks or not.…”

Section: Introductionmentioning

confidence: 99%

Mask Detection and Social Distance Identification Using Internet of Things and Faster R-CNN Algorithm

Meivel¹,

Sindhwani

Anand³

et al. 2022

Computational Intelligence and Neuroscience

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The drones can be used to detect a group of people who are unmasked and do not maintain social distance. In this paper, a deep learning-enabled drone is designed for mask detection and social distance monitoring. A drone is one of the unmanned systems that can be automated. This system mainly focuses on Industrial Internet of Things (IIoT) monitoring using Raspberry Pi 4. This drone automation system sends alerts to the people via speaker for maintaining the social distance. This system captures images and detects unmasked persons using faster regions with convolutional neural network (faster R-CNN) model. When the system detects unmasked persons, it sends their details to respective authorities and the nearest police station. The built model covers the majority of face detection using different benchmark datasets. OpenCV camera utilizes 24/7 service reports on a daily basis using Raspberry Pi 4 and a faster R-CNN algorithm.

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