İnsansız hava aracı ile gerçek zamanlı maske ve sosyal mesafe tespiti
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Dosyalar
Tarih
2023
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Yayıncı
Düzce Üniversitesi
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
COVID-19 korona virüsü ilk olarak Çin'in Wuhan kentinde, Aralık 2019'un sonlarında ortaya çıkmıştır. Genellikle bu virüs, küçük parçacıklar yoluyla, konuşma sırasında, öksürürken, hapşırırken ve çoğunlukla yakın temasta olan kişiler arasında, kapalı ve havalandırılmamış alanlarda bulaşır. Dünya sağlık örgütü, virüsün yayılmasını önlemenin en etkili yollarını, fiziksel mesafenin korunması ve maskenin takılması olarak belirlemiştir. Devlet kurumları ve yetkilileri, pandemi sürecinde okul, alışveriş merkezleri ve ulaşım tesisleri gibi halka açık ve kapalı alanlarda sosyal mesafenin yaklaşık 2 metre olarak tutulması ve maske takmayı zorunlu hale getirmiştir. Ancak, insanların maskelerini takıp takmadığının ve aralarındaki mesafenin otomatik olarak kontrolünün yapılması önemli bir problem haline gelmiştir. Bu çalışmada, günümüzde etkili olan COVID-19 gibi salgınların bulaş zincirini kırmak, salgının hızını yavaşlatmak adına önemli olan maske kullanımını tespit etmek için MobileNetV2 algoritması kullanılmıştır. Sosyal mesafe tespiti ise Yolov3 algoritması kullanılarak gerçekleştirilmiştir. Aynı zamanda, görüntülerin elde edilmesinde ve zamandan tasarruf ve daha kolay denetim sağlayabilmek için İnsansız Hava Aracı (İHA) kullanılmıştır. Yapılan uygulama sonuçlarında, önerilen mimarilerde %99,87 ile %100'e varan bir doğruluk elde edilmiştir. Anahtar Kelimeler: Maske tespiti, Sosyal mesafe, MobileNetV2, Yolov3, COVID-19, İHA
The COVID-19 corona virus first appeared in Wuhan, China, in late December 2019. Usually, this virus is transmitted through small particles when speaking, coughing, sneezing, and in confined and unventilated spaces, mostly between people in close contact. The World Health Organization has identified the most effective ways to prevent the spread of the virus as maintaining physical distance and wearing a mask. State institutions and authorities have made it mandatory to keep the social distance of approximately 2 meters and to wear masks in public and closed areas such as schools, shopping centers and transportation facilities during the pandemic process. However, automatic control of whether people are wearing masks and the distance between them has become an important problem. In this study, MobilNetV2 for mask detection and Yolov3 for social distance detection, which are important in breaking the transmission chain of epidemics such as COVID-19, which are effective today, and slowing the speed of the epidemic, were carried out using deep learning algorithm and image processing methods. At the same time, an Unmanned Aerial Vehicle (UAV) was used to obtain images and to save time and provide easier control. As a result of the application, an accuracy of 99.87% to 100% was obtained in the proposed architectures. Keywords: Mask detection, Social distancing, MobileNetV2, Yolov3, COVID-19, UA
The COVID-19 corona virus first appeared in Wuhan, China, in late December 2019. Usually, this virus is transmitted through small particles when speaking, coughing, sneezing, and in confined and unventilated spaces, mostly between people in close contact. The World Health Organization has identified the most effective ways to prevent the spread of the virus as maintaining physical distance and wearing a mask. State institutions and authorities have made it mandatory to keep the social distance of approximately 2 meters and to wear masks in public and closed areas such as schools, shopping centers and transportation facilities during the pandemic process. However, automatic control of whether people are wearing masks and the distance between them has become an important problem. In this study, MobilNetV2 for mask detection and Yolov3 for social distance detection, which are important in breaking the transmission chain of epidemics such as COVID-19, which are effective today, and slowing the speed of the epidemic, were carried out using deep learning algorithm and image processing methods. At the same time, an Unmanned Aerial Vehicle (UAV) was used to obtain images and to save time and provide easier control. As a result of the application, an accuracy of 99.87% to 100% was obtained in the proposed architectures. Keywords: Mask detection, Social distancing, MobileNetV2, Yolov3, COVID-19, UA
Açıklama
Anahtar Kelimeler
Elektrik ve Elektronik Mühendisliği, Electrical and Electronics Engineering
