SISTEM PENDETEKSI BUS DAN TRUK MENGGUNAKAN METODE DEEP LEARNING YOU ONLY LOOK ONCE ( YOLO )

DESFIAN, KHARISMA and Siswanti, Sri Desy and Ubaya, Huda (2020) SISTEM PENDETEKSI BUS DAN TRUK MENGGUNAKAN METODE DEEP LEARNING YOU ONLY LOOK ONCE ( YOLO ). Undergraduate thesis, Sriwijaya University.

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Abstract

In computer vision, there are several problems, including object detection and image classification. Deep Learning You Only Look Once (YOLO) which is an Artificial Intelegence model of an improved Artificial Neural Network (ANN) is one method that is being developed and is widely used for object detection. In this research, the Deep Learning You Only Look Once (YOLO) model is applied to detect two classes of detection object, which are buses and trucks. With training data using 120 images and test data using 30 images, with resolution 450x300 pixels. YOLO performance with a threshold of 0.25 has an accuracy value of 93.33%, a precision value of 92.30% and a recall value of 100%. YOLO performance with a threshold of 0.45 has an accuracy value of 96.66%, a precision value of 96.15% and a recall value of 100%. YOLO performance with a threshold of 0.85 has an accuracy value of 96.66%, a precision value of 96.15% and a recall value of 96%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Computer Vision, Object Detection, Deep Learning, You Only Look Once (YOLO)
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation. Q Science > QA Mathematics > QA8.9-QA10.3 Computer science. Artificial intelligence. Computational complexity. Data structures (Computer scienc. Mathematical Logic and Formal Languages T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7885-7895 Computer engineering. Computer hardware
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Users 10153 not found.
Date Deposited:	21 Jan 2021 08:03
Last Modified:	21 Jan 2021 08:03
URI:	http://repository.unsri.ac.id/id/eprint/40716

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