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Title:
Razvoj naprednega sistema za detektiranje voznih pasov na platformah GPU : master thesis
Authors:
ID
Crnek, Karlo
(Author)
ID
Rojc, Matej
(Mentor)
More about this mentor...
ID
Mlakar, Izidor
(Mentor)
More about this mentor...
Files:
MAG_Crnek_Karlo_2019.pdf
(3,48 MB)
MD5: 3BD144AC551685CAE9C9112C206E3470
PID:
20.500.12556/dkum/e84621ae-b288-4448-ad60-2723a111ce40
Language:
Slovenian
Work type:
Master's thesis/paper
Typology:
2.09 - Master's Thesis
Organization:
FERI - Faculty of Electrical Engineering and Computer Science
Abstract:
Problem, ki ga obravnavamo v magistrski nalogi je detektiranje voznih pasov na RGB slikah oz. posnetkih ceste pred vozilom med vožnjo. Za rešitev tega problema smo se odločili uporabiti tehnike »globokega učenja«, predvsem konvolucijske nevronske mreže, s katerimi smo izvedli semantično segmentiranje. Problem smo reševali s tremi različnimi arhitekturami nevronskih mrež, ki smo jih učili na naboru podatkov BDD100k. Modele mrež smo nato testirali in primerjali rezultate s pomočjo IoU metrike za semantično segmentacijo. Opravili smo tudi več eksperimentov s ciljem izboljšanja IoU vrednosti in generalizacije modelov. Na koncu smo modele testirali tudi na Nvidia Jetson TX2 platformi in predlagali možnost vključitve takšnih modelov v sistem avtonomnega vozila.
Keywords:
globoko učenje
,
konvolucijske nevronske mreže
,
segmentacija voznega pasu
,
strojni vid
,
avtonomno vozilo
Place of publishing:
Maribor
Place of performance:
Maribor
Publisher:
[K. Crnek]
Year of publishing:
2019
Number of pages:
XVII, 84 f.
PID:
20.500.12556/DKUM-75089
UDC:
621.396.969.3:004.89(043.2)
COBISS.SI-ID:
22831638
NUK URN:
URN:SI:UM:DK:HACS1X2M
Publication date in DKUM:
13.11.2019
Views:
1890
Downloads:
183
Metadata:
Categories:
KTFMB - FERI
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:
CRNEK, Karlo, 2019,
Razvoj naprednega sistema za detektiranje voznih pasov na platformah GPU : master thesis
[online]. Master’s thesis. Maribor : K. Crnek. [Accessed 22 March 2025]. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=75089
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Licences
License:
CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Link:
http://creativecommons.org/licenses/by-nc-nd/4.0/
Description:
The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.
Licensing start date:
20.09.2019
Secondary language
Language:
English
Title:
Development of an advanced system for lane detection on GPU platforms
Abstract:
The problem we are dealing with in this master’s thesis is lane detection on RGB images, i.e. images of the road in front of the vehicle during driving. To solve this problem, we have used “deep learning” techniques, specifically convolutional neural networks for the semantic segmentation task. We designed three different network architectures, which were trained on BDD100k dataset. Those network models were then evaluated and compared based on IoU metric used for semantic segmentation. We performed several experiments with the goal of improving IoU results and the generalization of the models. Finally, we tested the models on the Nvidia Jetson TX2 platform and proposed the possibilities for incorporating such models into the autonomous vehicle system.
Keywords:
deep learning
,
convolutional neural networks
,
lane segmentation
,
computer vision
,
autonomous vehicles
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