Derin öğrenme ile 3B nokta bulutlarının sınıflandırılmasına genel bir bakış

Muhammed Ahmet Demirtaş

doi:10.24012/dumf.1067736

Derin öğrenme ile 3B nokta bulutlarının sınıflandırılmasına genel bir bakış

Abstract

Nokta bulutu (NB) bir vektör uzayında nesneye ait bilgilerin x,y,z koordinat sisteminde matematiksel olarak temsil edilen noktalar kümesidir. Noktalar kaydedilen uzaysal koordinat sisteminde sınıflandırılarak nesne ya da bir alanı ifade eden anlamsal bilgileri tanımlarlar. 3 boyutlu (3B) nokta bulutu gelişen teknolojilerle beraber nesneleri sınıflandırma, algılama ve tanıma alanlarında son zamanlarda oldukça popüler bir hale gelmiştir. Lazer tarama sistemleri ile taranan nesneler 3B nokta bulutuna dönüştürülmüş ve bu verilerin sanal ortama aktarılması ile farklı veri setleri elde edilmiştir. 3B noktasal verileri derin ağlar ile başarılı bir şekilde sınıflandırmak için geliştirilen yöntemler detaylı olarak incelenmiştir. Nokta bulutlarına 3B koordinat sistemi ile birlikte farklı bilgiler dâhil ederek (derinlik ya da RGB(red-green-blue)) farklı boyutlarda veya farklı yoğunlukta nokta bulutları oluşturulmuştur. Ayrıca nokta bulutu veri kümesindeki her bir noktaya ait; harici veya dâhili bilgiler eklenmiş RGB değerleri ile nesneler renklendirilmiştir. Bu araştırmada 3B nokta bulutunu derin ağlar ile sınıflandıran yöntemlerin başarı performansları, avantajları, dezavantajları analiz edilmiştir. Özellikle uygulanan algoritmalar, denenen yöntemler ve oluşturulan modeller karşılaştırılmış ve tartışılmıştır. Son olarak makalede gelecekteki çalışmalara hız ve yön vermesi için güncel yöntemler kapsamlı bir şekilde sunulmuştur.

Keywords

References

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Details

Primary Language

Turkish

Subjects

-

Journal Section

Review

Authors

Muhammed Ahmet Demirtaş ^*
0000-0003-4092-7284
Türkiye

Publication Date

March 30, 2022

Submission Date

February 3, 2022

Acceptance Date

March 25, 2022

Published in Issue

Year 2022 Volume: 13 Number: 1

DOI

https://doi.org/10.24012/dumf.1067736

IZ

https://izlik.org/JA67JX64EA

Cite

RIS / Bibtex

APA

Demirtaş, M. A. (2022). Derin öğrenme ile 3B nokta bulutlarının sınıflandırılmasına genel bir bakış. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 13(1), 1-9. https://doi.org/10.24012/dumf.1067736

AMA

1.Demirtaş MA. Derin öğrenme ile 3B nokta bulutlarının sınıflandırılmasına genel bir bakış. DUJE. 2022;13(1):1-9. doi:10.24012/dumf.1067736

Chicago

Demirtaş, Muhammed Ahmet. 2022. “Derin öğrenme Ile 3B Nokta Bulutlarının Sınıflandırılmasına Genel Bir Bakış”. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi 13 (1): 1-9. https://doi.org/10.24012/dumf.1067736.

EndNote

Demirtaş MA (March 1, 2022) Derin öğrenme ile 3B nokta bulutlarının sınıflandırılmasına genel bir bakış. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi 13 1 1–9.

IEEE

[1]M. A. Demirtaş, “Derin öğrenme ile 3B nokta bulutlarının sınıflandırılmasına genel bir bakış”, DUJE, vol. 13, no. 1, pp. 1–9, Mar. 2022, doi: 10.24012/dumf.1067736.

ISNAD

Demirtaş, Muhammed Ahmet. “Derin öğrenme Ile 3B Nokta Bulutlarının Sınıflandırılmasına Genel Bir Bakış”. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi 13/1 (March 1, 2022): 1-9. https://doi.org/10.24012/dumf.1067736.

JAMA

1.Demirtaş MA. Derin öğrenme ile 3B nokta bulutlarının sınıflandırılmasına genel bir bakış. DUJE. 2022;13:1–9.

MLA

Demirtaş, Muhammed Ahmet. “Derin öğrenme Ile 3B Nokta Bulutlarının Sınıflandırılmasına Genel Bir Bakış”. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, vol. 13, no. 1, Mar. 2022, pp. 1-9, doi:10.24012/dumf.1067736.

Vancouver

1.Muhammed Ahmet Demirtaş. Derin öğrenme ile 3B nokta bulutlarının sınıflandırılmasına genel bir bakış. DUJE. 2022 Mar. 1;13(1):1-9. doi:10.24012/dumf.1067736

Cited By

İnsansız Hava Araçlarında Nokta Bulutu Verisi Kullanılarak İniş Pisti Uygunluk Analizi

Fırat Üniversitesi Mühendislik Bilimleri Dergisi

https://doi.org/10.35234/fumbd.1393959