Open AccessObject Detection Frameworks fo Real-Time, Scale-Invariant Face Mask Detection
Author(s) -
Louis Philippe B. Facun,
Maria Jeseca C. Baculo,
Marlon F. Libao,
Ceazar M. Eisma,
Christian B. Fredeluces,
Rigzor A. Garlejo,
Raymart S. Idio
Publication year - 2022
Publication title -
international journal of future computer and communication
Language(s) - English
Resource type - Journals
ISSN - 2010-3751
DOI - 10.18178/ijfcc.2022.11.1.582
Subject(s) - computer science , inference , artificial intelligence , invariant (physics) , crowds , face detection , face (sociological concept) , computer vision , object detection , covid-19 , scale (ratio) , pattern recognition (psychology) , real time computing , machine learning , facial recognition system , computer security , cartography , medicine , social science , physics , disease , pathology , sociology , infectious disease (medical specialty) , mathematical physics , geography
As the world experiences a pandemic caused by the spread of COVID-19, following health protocols like the proper wearing of face masks is essential to help prevent the spread of the virus as stressed by the World Health Organization (WHO). In the Philippines, manual monitoring of these measures may not be as efficient when handling large crowds. The authors were able to generate a real-time, scale-invariant face mask recognition model that also handles occlusions. It was trained using images from the wild to simulate real-world conditions. Existing state-of-the-art object detection frameworks such as MobileNet, RetinaNet, and YOLOv4 were implemented to detect and localize the proper use of face masks in public. To evaluate the performance of the models, the mAP, inference time, and precision curve were used. The performance of the model has resulted to as high as .94 mAP with an inference time of 73 ms.