IMPLEMENTASI METODE FASTER REGION CONVOLUTIONAL NEURAL NETWORK UNTUK DETEKSI KAPAL LAUT

ARIADI, KRISTI and Passarella, Rossi (2024) IMPLEMENTASI METODE FASTER REGION CONVOLUTIONAL NEURAL NETWORK UNTUK DETEKSI KAPAL LAUT. Undergraduate thesis, Sriwijaya University.

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Abstract

Accurate and efficient ship detection has become an urgent necessity amid increasing maritime activities, including security monitoring, law enforcement, and maritime traffic management. This study aims to implement the Faster R-CNN (Region-based Convolutional Neural Network) method for ship detection to improve efficiency and accuracy compared to conventional methods. The data used in this study consists of 693 ship images. This research also analyzes the performance of the Faster R-CNN method under various image conditions and identifies factors influencing detection performance. The results of this study are expected to make a significant contribution to the development of object detection technology in maritime environments, particularly for security and traffic management applications.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Deteksi Kapal Laut, Faster R-CNN, Pembelajaran Mesin, Pengolahan Citra
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Kristi Ariadi
Date Deposited:	07 Jan 2025 08:35
Last Modified:	07 Jan 2025 08:35
URI:	http://repository.unsri.ac.id/id/eprint/162740

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