Open AccessContactless System with Mask and Temperature Detection
Author(s) -
Sheetal Mahadik,
Namrata J. Ravat,
Kunal Singh,
Suvita K. Yadav
Publication year - 2021
Publication title -
international journal of advanced research in science, communication and technology
Language(s) - English
Resource type - Journals
ISSN - 2581-9429
DOI - 10.48175/ijarsct-v4-i3-002
Subject(s) - fuse (electrical) , computer science , focus (optics) , face (sociological concept) , deep learning , artificial intelligence , detector , feature (linguistics) , intersection (aeronautics) , computer vision , pyramid (geometry) , object detection , face detection , feature extraction , facial recognition system , pattern recognition (psychology) , telecommunications , engineering , electrical engineering , optics , social science , linguistics , philosophy , physics , aerospace engineering , sociology
Coronavirus disease in 2019 has affected the world very badly on a large scale. One of the important protection methods is to wear masks in public areas. Also, while using public services it is important to wear a mask correctly if you want to use their services. However, there is very few researches on face mask detection based on image analysis. In this paper, we propose Face Mask, which is a high-accuracy and efficient face mask detector. The proposed system is a one-stage detector, which consists of a pyramid network to fuse high-level semantic information with multiple feature maps, and a module to focus on detecting face masks. In addition, we also propose a novel cross-class object removal algorithm that will reject predictions with low confidences and the high intersection of the union. Besides, we also focus on the possibilities of implementing Face Mask with a light-weighted neural network MobileNet for embedded or mobile devices. In this paper, we introduce an affordable solution aiming to increase COVID-19 indoor safety, covering relevant aspects: 1) contactless temperature sensing 2) mask detection. Contactless temperature sensing subsystem relies on Arduino Uno using an infrared sensor or thermal camera, while mask detection is performed by leveraging computer vision techniques and Deep Learning Techniques.