Skripsi

DETEKSI JENIS KENDARAAN BERMOTOR DENGAN ALGORITMA DETECTION TRANSFORMER (DETR)

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Penilaian

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Increased traffic in the city of Palembang has caused problems such as congestion and accidents, necessitating an accurate vehicle detection system. This study proposes the use of DETR and RT-DETR to identify vehicle types in traffic images, with ResNet-50 and ResNet-101 as comparison architectures. The dataset consists of 1,233 images extracted from traffic videos in Palembang. The model was trained using PyTorch Lightning and a GPU for computational efficiency. Evaluation was conducted using AP, mAP, AR, and mAR metrics. The results show that RT-DETR with the ResNet-101 backbone and a batch size of 4 provides the best performance, with mAP of 0.558 and mAR of 0.221. This study demonstrates that architecture selection significantly impacts accuracy and can serve as a foundation for the development of intelligent transportation systems in the future.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507005408	T182773	T1827732025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1827732025

Publisher

Indralaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xvi, 98 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

006.370 7

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

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Subject(s)

Prodi Sistem Komputer
Deteksi Jenis Kendaraan

Specific Detail Info

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Statement of Responsibility

KA

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