Razpoznava drevesnih vrst iz oblakov točk z uporabo globokih nevronskih mrež : magistrsko delo

Topolovec Klemenčič, Vid

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Title:	Razpoznava drevesnih vrst iz oblakov točk z uporabo globokih nevronskih mrež : magistrsko delo
Authors:	ID Topolovec Klemenčič, Vid (Author) ID Kohek, Štefan (Mentor) More about this mentor... ID Strnad, Damjan (Comentor)
Files:	MAG_Topolovec_Klemencic_Vid_2024.pdf (16,54 MB) MD5: 084B79CD1A9EA1A22CB65D76A04755C1 MAG_Topolovec_Klemencic_Vid_2024.zip (4,31 MB) MD5: FCCD9DFC57AF5DDB341284B0BE7B88A0
Language:	Slovenian
Work type:	Master's thesis/paper
Typology:	2.09 - Master's Thesis
Organization:	FERI - Faculty of Electrical Engineering and Computer Science
Abstract:	V magistrskem delu obravnavamo razpoznavo drevesnih vrst iz oblakov točk z uporabo novejših nevronskih mrež in primerjamo uspešnost razpoznave s sorodnimi deli. Razpoznavo smo zasnovali na dva različna načina. Pri prvem načinu smo izbrali in pripravili nevronsko mrežo za obdelavo 3D podatkov oz. oblakov točk, medtem ko smo pri drugem načinu izbrali in pripravili nevronsko mrežo za obdelavo 2D podatkov oz. slik. Pripravili smo tudi skupno podatkovno zbirko z združitvijo prosto dostopnih zbirk, ki vsebujejo posamezna drevesa v obliki oblakov točk, in med učenjem obogatili podatke. Po zaključenem učenju s pripravljeno skupno podatkovno zbirko nismo dosegli podobnih zaključkov kot v primerjanem predhodnem delu. V našem primeru je izbrana nevronska mreža, namenjena obdelavi 3D podatkov, dosegla 4 % višjo skupno točnost od izbrane nevronske mreže, ki je obdelovala 2D podatke.
Keywords:	globoke nevronske mreže, klasifikacija drevesnih vrst, 3D oblaki točk, 2D slike, obogatitev podatkov
Place of publishing:	Maribor
Place of performance:	Maribor
Publisher:	[V. Topolovec Klemenčič]
Year of publishing:	2024
Number of pages:	1 spletni vir (1 datoteka PDF (XI, 91 f.))
PID:	20.500.12556/DKUM-88822
UDC:	004.93:004.032.26(043.2)
COBISS.SI-ID:	206147843
Publication date in DKUM:	01.07.2024
Views:	376
Downloads:	113
Metadata:
Categories:	KTFMB - FERI
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Licences

License:	CC BY 4.0, Creative Commons Attribution 4.0 International

Link:	http://creativecommons.org/licenses/by/4.0/
Description:	This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:	27.05.2024

Secondary language

Language:	English
Title:	Tree species recognition from point clouds using deep neural networks
Abstract:	In this thesis we discuss the classification of tree species from point clouds using more recent neural networks and compare the success of the classification with related work. We have divided the classification into two distinct parts. For the first part, we selected and prepared a neural network for processing 3D data or point clouds. For the second part, we selected and prepared a neural network for processing 2D data or images. We also created a common database by merging freely available databases containing individual trees in the form of point clouds and augmenting the data during the learning process. After completing the study with the prepared common database, we did not reach the same conclusions as in the related work. In our example, the neural network selected for 3D data processing achieved a 4 % higher overall accuracy than the neural network selected for 2D data processing.
Keywords:	deep neural networks, tree species classification, 3D point clouds, 2D images, data augmentation

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