Mask Detection and Social Distance Identification Using Internet of Things and Faster R-CNN Algorithm. (1st February 2022)

• Record Type: Journal Article
• Authors: Meivel, S.; Sindhwani, Nidhi; Anand, Rohit; Pandey, Digvijay; Alnuaim, Abeer Ali; Altheneyan, Alaa S.; Jabarulla, Mohamed Yaseen; Lelisho, Mesfin Esayas
• Other Names: Singal Gaurav Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2022(2022)
• Publication Date: 2022-02-01
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2022/2103975
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : 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.