Application of Deep-Learning Methods to Real Time Face Mask Detection
| Autor: | R. Gastón Araguás, Daiana García, Javier A. Redolfi, Diego Gonzalez Dondo |
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| Rok vydání: | 2021 |
| Předmět: |
Scheme (programming language)
General Computer Science Artificial neural network business.industry Computer science Deep learning Detector 02 engineering and technology 010501 environmental sciences 01 natural sciences Facial recognition system Object detection Face (geometry) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence Electrical and Electronic Engineering business Personal protective equipment computer 0105 earth and related environmental sciences computer.programming_language |
| Zdroj: | IEEE Latin America Transactions. 19:994-1001 |
| ISSN: | 1548-0992 |
| Popis: | Due to the high rate of infection and the lack of a specific vaccine or medication for the new disease known as SARS-CoV2, the World Health Organization (WHO) has recommended the use of Personal Protective Equipment (PPE) as the main measure to avoid or reduce infections. One way to maximize compliance with this recommendation is through an automatic system that can recognize in real time whether a person is correctly using the corresponding PPE. This work presents the design, implementation and performance analysis of a system for recognizing the use of masks from image sequences, with the ability to operate in real time. Based on a generic object detection network, a training scheme is proposed for a detector of faces with masks and faces without masks, wherewith an average detection accuracy higher than 90% is obtained. This accuracy can be improved by using a network with a greater number of parameters, but with a longer computation time. The performance of the detector is validated with video sequences of people with and without facemasks, captured in different environments. |
| Databáze: | OpenAIRE |
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