Robotic based mask detection to prevent epidemic diseases transmitted through droplets using pre-trained deep learning models

Year 2023, , 762 - 769, 15.07.2023

Ali Ünlütürk

https://doi.org/10.28948/ngumuh.1291781

Abstract

The Coronavirus disease, which emerged in Wuhan, China in December 2019 and spread rapidly all over the world, infected healthy people by being transmitted by small droplets. Medical experts have stated that the most effective fight against the Coronavirus disease is the need for people in contact to wear masks. Despite this, some people violated the obligation to wear masks. In this study, mask detection performances of pre-trained Convolutional Neural Network (CNN) models such as NasNetMobile, MobileNetV3Small, ResNet50, DenseNet121 and EfficientNetV2B0, which were previously trained, were evaluated in order to automatically detect people who violate the mask wearing obligation. At the end of this evaluation, DenseNet121 architechture has become the most successful model. This model has been tested with the image obtained from the camera on a robotic system with six Degrees of Freedom (6-DOF). The human face images taken from the camera were processed using the Jetson Xavier NX development board. As a result, this study will help the officers who carry out mask inspections in public areas and will significantly reduce the spread of new outbreaks similar to the Coronavirus.

Keywords

Mask detection, Epidemic diseases, Convolutional neural network, Transfer learning, Robotic

Önceden eğitilmiş derin öğrenme modelleri kullanılarak damlacık yoluyla bulaşan salgın hastalıkları önlemek için robotik tabanlı maske tespiti

Abstract

Aralık 2019’da Çin’in Wuhan şehrinde ortaya çıkan ve tüm dünyada hızla yayılan Koronavirüs hastalığı solunum yolu sonucu oluşan küçük damlacıklar ile bulaşarak sağlıklı insanları enfekte etmiştir. Tıp uzmanları Koronavirüs hastalığına karşı en etkili mücadelenin temas halindeki kişilerin maske takması gerekliliğini belirtmişlerdir. Buna rağmen bazı kişiler maske takma zorunluluğunu ihlal etmişlerdir. Bu çalışmada maske takma zorunluluğunu ihlal eden kişilerin otomatik olarak tespit edilebilmesi için önceden eğitilmiş olan NaNetMobile, MobileNetV3Small, ResNet50, DenseNet121 ve EfficientNetV2B0 gibi derin sinir ağı modellerinin maske tanıma performansları değerlendirilmiştir. Bu değerlendirme sonucunda en başarılı DenseNet121 modeli ele edilmiştir. Bu model 6- Serbestlik Derecesine (6-DOF) sahip robotik bir sisteminin üzerinde yer alan kameradan elde edilen görüntü ile doğrulanmıştır. Kameradan alınan insane ait yüz görüntüleri yüksek kare hızlarında NVIDIA tarafından geliştirilen Jetson Xavier geliştirme kartı kullanılarak işlenmiştir. Sonuç olarak, bu çalışma toplu alanlarda maske denetimi gerçekleştiren görevlilere yardımcı olacak ve Koronavirüs benzeri çıkabilecek yeni salgınların yayılımı önemli ölçüde azaltacaktır.

Keywords

Maske Tanıma, Epidemik hastalıklar, Evrişimli sinir ağı, Transfer öğrenme, Robotik

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