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

Yunus Eği

Article Details

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

Authors:

Yunus Eği

Publisher:

Igdir University

Main Topics:

Bilgisayar Yazılımı | Elektrik Mühendisliği

Journal:

Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.13 Issue 1, pp.22-32

Document Type:

Article

Publication Date:

1/2023

Document Language:

English

Document ID (Org No.):

2001284100

pISSN:

2536-4618

Keywords:

Abstract:

 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.

 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.

 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.

 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.

Article Details

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

Authors:

Yunus Eği

Publisher:

Igdir University

Main Topics:

Bilgisayar Yazılımı | Elektrik Mühendisliği

Journal:

Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.13 Issue 1, pp.22-32

Document Type:

Article

Publication Date:

1/2023

Document Language:

English

Document ID (Org No.):

2001284100

pISSN:

2536-4618

Deep Learning

YOLO V7

Covid-19

Mask Detection

Warning System

Derin Öğrenme

YOLO V7

Covid 19

Maske Tanımlaması

Uyarı Sistemi

Issues

Article Details

Authors: Yunus Eği

Publisher: Igdir University

Main Topics: Bilgisayar Yazılımı | Elektrik Mühendisliği

Journal: Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.13 Issue 1, pp.22-32

Document Type: Article

Publication Date: 1/2023

Document Language: English

Document ID (Org No.): 2001284100

pISSN: 2536-4618

Issues

Authors:

Yunus Eği

Publisher:

Igdir University

Main Topics:

Bilgisayar Yazılımı | Elektrik Mühendisliği

Journal:

Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.13 Issue 1, pp.22-32

Document Type:

Article

Publication Date:

1/2023

Document Language:

English

Document ID (Org No.):

2001284100

pISSN:

2536-4618