Skripsi

PENGENALAN OBJEK SECARA REAL TIME MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORKS MODEL YOLOV4-TINY

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Penilaian

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Object recognition is a technology related to Computer Vision which can detect and recognize objects in digital images. However, the process of detecting and recognizing objects is something that is very difficult to do by a computer due to the many factors that can affect the shape of the object in a digital image. In this study, researchers will discuss object recognition in real time using convolution neural networks. The purpose of this study is to find out how the CNN algorithm works in detecting and recognizing objects in digital images in Real Time on Mobile Phone devices and also to find out the level of accuracy and Processing Time of the CNN model implemented on Mobile phone devices. The results of this study indicate that the YOLOv4-tiny CNN model can optimally process object recognition at a threshold of 0.5 with a Precision value of 0.93, Recall of 0.93, Accuracy of 0.86, F1-Score of 0.93, and mAP of 94.27%. The average Processing Time on the software in carrying out the Real Time object recognition process is 427ms.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307004995	T128146	T1281462023	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1281462023

Publisher

Inderalaya : Jurusan Teknik Informatika, Fakultas Ilmu Komputer Universitas Sriwijaya., 2023

Collation

xv, 40 hlm.; Ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.650 7

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

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Subject(s)

Jurusan Teknik Informatika
Jaringan Komunikasi Komputer

Specific Detail Info

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Statement of Responsibility

SEPTA

Other version/related

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