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Combating COVID-19 using object detection techniques for next-generation autonomous systems

COVID-19 has become a global crisis. During such a time of adversity, it has become difficult to create safe working conditions for people resulting in a lack of workforce for performing a multitude of tasks. As a result, there is a requirement for “next-gen” autonomous systems to perform various ta...

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Detalles Bibliográficos
Autores principales: Shenai, Hrishikesh, Gala, Jay, Kekre, Kaustubh, Chitale, Pranjal, Karani, Ruhina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9261876/
http://dx.doi.org/10.1016/B978-0-12-824557-6.00007-8
Descripción
Sumario:COVID-19 has become a global crisis. During such a time of adversity, it has become difficult to create safe working conditions for people resulting in a lack of workforce for performing a multitude of tasks. As a result, there is a requirement for “next-gen” autonomous systems to perform various tasks. One of the reasons why humans are efficient in doing a lot of tasks is the ability to detect and distinguish between various objects around them and proceed with doing the intended task further. Object detection methods are designed to replicate this human behavior and can be used in various applications that serve to aid in the COVID-19 crisis. Object detection deals with identifying objects belonging to a predefined class in the image. This chapter aims at explaining the most commonly used methods like region-based convolutional neural network and You Only Look Once of object detection along with a few of its applications.