Skripsi

PENGENALAN WAJAH BERMASKER SECARA REAL-TIME MENGGUNAKAN METODE YOLOv5

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Penilaian

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The government requires people to wear face masks to reduce the transmission of the COVID-19 virus. Face recognition system becomes less effective at recognizing masked faces. In this research, a software is developed to recognize masked face in real-time using the YOLOv5 method. Training of the model was done with 9 different configurations of epoch and batch size, of which the best result was taken and used for testing. Testing was done using images and real-time input. The maximum accuracy of identification using image is 100% while the maximum accuracy of real-time identification is 64%. While running the experiment, it is found that the brightness of the room affects the performance of YOLOv5. When the brightness is drastically different, it is more difficult to identify the individual.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307003579	T87125	T871252023	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T871252023

Publisher

Inderalaya : Jurusan Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2022

Collation

xx, 334 hlm.; Ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

005.307

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

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Subject(s)

Aplikasi perangkat lunak
Jurusan Teknik Informatika

Specific Detail Info

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Statement of Responsibility

SEPTA

Other version/related

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