Yunus EĞİ

Kalabalık Kamu Alanları için YOLO V7 ve Bilgisayar Görmesi Temelli Maske Giyim Uyarı Sistemi

Hastane, okul, alışveriş merkezi gibi insanların bir arada olması gereken kalabalık alanlarda sosyal mesafe ve maske takma kurallarına uyulmaması nedeniyle dünya genelinde Covid 19 vakalarının etkisi artıyor. Yetkililer her ne kadar maske takılmamasını engellemek için çeşitli önlemler alsalar da kalabalık ortamlarda maske denetlemesi güç olmaktadır. İnsan eli ile yapılan denetimlerde maske takmayan kişiler gözden kaçabilmekte olup bu durum salgının artışında önemli bir etken olmaktadır. Bu çalışmanın amacı yoğun insan trafiğinin olduğu kalabalık ortamlarda insanların Covid-19 salgınından korunmalarını sağlamak için son teknolojik algoritma olan YOLO

Anahtar Kelimeler:

Derin Öğrenme, YOLO V7, Covid 19, Maske Tanımlaması, Uyarı Sistemi

YOLO V7 and Computer Vision-Based Mask-Wearing Warning System for Congested Public Areas

The impact of Covid 19 cases is increasing worldwide due to not complying with social distancing and mask-wearing rules in congested areas such as hospitals, schools, and malls where people have to be together. Although the authorities have taken various precautions to prevent not wearing masks, it is challenging to inspect masks in crowded areas. People who do not wear masks can be unnoticed by visual inspections, which is a critical factor in the increase of the epidemic. This study aims to create an Artificial Intelligence (AI) based mask inspection system with the YOLO V7 deep learning method to ensure that overcrowded public areas are protected from the Covid-19 epidemic.

Keywords:

Deep Learning, YOLO V7, Covid-19, Mask Detection, Warning System,

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