Zaznava in lociranje malin z uporabo YOLO algoritma : magistrsko delo

Kenda, Urban

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Title:	Zaznava in lociranje malin z uporabo YOLO algoritma : magistrsko delo
Authors:	ID Kenda, Urban (Author) ID Šafarič, Riko (Mentor) More about this mentor... ID Klančnik, Simon (Mentor) More about this mentor... ID Rakun, Jurij (Comentor)
Files:	MAG_Kenda_Urban_2023.pdf (1013,36 KB) MD5: FCF35F0731C4B1D51224B12BA6A6555C
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 smo raziskali delovanje LiDAR senzorjev ter uporabo umetne inteligence v strojnem vidu, vključno z nevronskimi mrežami, konvolucijskimi nevronskimi mrežami (CNN) in algoritmi YOLOv3, v4 in v4-tiny. V praktičnem delu smo testirali vse tri algoritme in nato izbrali najuspešnejšega, YOLOv4, ter ga dodatno analizirali. Preverili smo hitrost algoritmov ter razvili algoritem, ki je na podlagi oblakov točk in kamere sposoben določiti lokacijo malin. Ugotovili smo, da je uporaba LiDAR senzorjev v kombinaciji z umetno inteligenco učinkovita pri zaznavanju in lociranju malin v 3D-prostoru. Najuspešnejši algoritem YOLOv4 je bil sposoben razvrstiti zrele in nezrele maline z natančnostjo 84,13 %. Naš razviti algoritem je omogočil določanje lokacije malin s kombinirano uporabo oblakov točk in kamere ter tako skoraj v polovici izmerjenih primerov določil lokacijo z napako, manjšo od 2 cm.
Keywords:	malina, strojni vid, YOLO, nevronska mreža, CNN, oblak točk
Place of publishing:	Maribor
Place of performance:	Maribor
Publisher:	[U. Kenda]
Year of publishing:	2023
Number of pages:	1 spletni vir (1 datoteka PDF (VII, 48 f.))
PID:	20.500.12556/DKUM-84362
UDC:	004.8.021:528.8.044.6(043.2)
COBISS.SI-ID:	158081283
Publication date in DKUM:	15.06.2023
Views:	665
Downloads:	148
Metadata:
Categories:	KTFMB - FERI
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Licences

License:	CC BY-NC 4.0, Creative Commons Attribution-NonCommercial 4.0 International

Link:	http://creativecommons.org/licenses/by-nc/4.0/
Description:	A creative commons license that bans commercial use, but the users don’t have to license their derivative works on the same terms.
Licensing start date:	30.05.2023

Secondary language

Language:	English
Title:	Detection and localization of raspberries using the YOLO algorithm
Abstract:	In this master's thesis, we explored the operation of LiDAR sensors and the use of artificial intelligence in machine vision, including neural networks, convolutional neural networks (CNN), and YOLOv3, v4, and v4-tiny algorithms. In the practical part, we tested all three algorithms and then selected the most successful one, YOLOv4, and further analyzed it. We also checked the speed of the algorithms and developed an algorithm capable of determining the location of raspberries based on point clouds and a camera. We found that the use of LiDAR sensors in combination with artificial intelligence is effective in detecting and locating raspberries in 3D space. The most successful YOLOv4 algorithm was able to detect ripe and raw raspberries with an accuracy of 84.13%. Our developed algorithm enabled the determination of the location of raspberries using a combination of point clouds and a camera and thus determined the location with an error of less than 2 cm in almost half of the measured cases.
Keywords:	raspberry, machine vision, YOLO, neural netwok, CNN, point cloud

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