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Mask Detection and Categorization during the COVID-19 Pandemic Using Deep Convolutional Neural Network
Detección y categorización de máscaras durante la pandemia del COVID-19 utilizando una red neuronal convolucional profunda
DOI:
https://doi.org/10.15446/ing.investig.101817Keywords:
Classification, Deep Learning, Mask Detection, COVID-19 (en)COVID-19, aprendizaje profundo, clasificación, detección de mascarillas (es)
With COVID-19 spreading all over the world and restricting our daily lives, the use of face masks has become very important, as it is an efficient way of slowing down the spread of the virus and an important piece to continue our daily tasks until vaccination is completed. People have been fighting this disease for a long time, and they are bored with the precautions, so they act carelessly. In this case, automatic detection systems are very important to keep the situation under control. In this research, deep learning models are trained with as little input data as possible in order to obtain an accurate face mask-wearing condition classification. These classes are mask-correct, mask wrong, and no mask, which refers to proper face mask use, improper face mask use, and no mask use, respectively. DenseNets, EfficientNets, InceptionResNetV2, InceptionV3, MobileNets, NasNets, ResNets, VGG16, VGG19, and Xception are the networks used in this study. The highest accuracy was obtained by the InceptionResNetV2 and Xception networks, with 99,6%. When other performance parameters are taken into consideration, the Xception network is a step forward. VGG16 and VGG19 also show an accuracy rate over 99%, with 99,1 and 99,4%, respectively. These two networks also had higher FPS and the two lowest initialization times during implementation. A comparison with recent studies was also carried out to evaluate the obtained accuracy. It was found that a higher accuracy can be obtained with the possible minimum input size.
Con el COVID-19 extendiéndose por todo el mundo y restringiendo nuestra vida diaria, el uso de mascarillas se ha vuelto muy importante, pues es una forma eficiente de frenar la propagación del virus, y una pieza importante para continuar con nuestras tareas diarias hasta que se complete la vacunación. La gente ha estado luchando contra la enfermedad durante mucho tiempo y se aburre con las precauciones, por lo que actúa con descuido. En este caso, los sistemas de detección automática son muy importantes para mantener la situación bajo control. En esta investigación se entrenan modelos de aprendizaje profundo con el mínimo de datos de entrada posibles para obtener una clasificación precisa de las condiciones de uso de las mascarillas. Estas clases son mask-correct, mask-wrong y no mask, que se refieren al uso adecuado, a un uso inadecuado y al no uso de la mascarilla facial, respectivamente. DenseNets, EfficientNets, InceptionResNetV2, InceptionV3, MobileNets, NasNets, ResNets, VGG16, VGG19 y Xception son las redes utilizadas en este estudio. La mayor precisión la obtuvieron las redes InceptionResNetV2 y Xception, con un 99,6 %. Cuando se tienen en cuenta otros parámetros de rendimiento, la red Xception un paso adelante. VGG16 y VGG19 presentan una tasa de precisión superior al 99 %, con 99,1 y 99,4 % respectivamente. Estas dos redes también presentaron FPS más altos y los dos tiempos de inicialización más bajos en la implementación. También se realizó una comparación con estudios recientes para evaluar la precisión obtenida. Se encontró que se puede obtener una mayor precisión con el mínimo tamaño de entrada posible.
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1. Devrim Kayali, Kamil Dimililer. (2025). Intelligent Informatics. Smart Innovation, Systems and Technologies. 389, p.29. https://doi.org/10.1007/978-981-97-2147-4_3.
2. Rasha J. H. Habeeb, Kamil Dimililer. (2025). Asia Pacific Advanced Network. Communications in Computer and Information Science. 2412, p.118. https://doi.org/10.1007/978-3-031-89813-6_8.
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Copyright (c) 2023 Kamil Dimililer, Devrim Kayali
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