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master's thesis
Duboko učenje za detekciju cestovne trake
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. urn:nbn:hr:168:195213
Blažević, Romana
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

Cite this document

Blažević, R. (2022). Duboko učenje za detekciju cestovne trake (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:195213

Blažević, Romana. "Duboko učenje za detekciju cestovne trake." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:195213

Blažević, Romana. "Duboko učenje za detekciju cestovne trake." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2022. https://urn.nsk.hr/urn:nbn:hr:168:195213

Blažević, R. (2022). 'Duboko učenje za detekciju cestovne trake', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 25 August 2024, https://urn.nsk.hr/urn:nbn:hr:168:195213

Blažević R. Duboko učenje za detekciju cestovne trake [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2022 [cited 2024 August 25] Available at: https://urn.nsk.hr/urn:nbn:hr:168:195213

R. Blažević, "Duboko učenje za detekciju cestovne trake", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:168:195213

Metadata
TitleDuboko učenje za detekciju cestovne trake
Title (english)Deep Learning for Road Lane Detection
AuthorRomana Blažević
MentorSven Lončarić (mentor)
Committee memberSven Lončarić (predsjednik povjerenstva)
Committee memberMarko Subašić (član povjerenstva)
Committee memberTomislav Petković (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2022-07-07, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Electrical Engineering
Abstract
Duboke neuronske mreže su vrlo moćni alati za vizualnu analitiku, odnosno interpretaciju i analizu slika, te su pokazale vrhunske performanse u raznim zadacima, pa tako i u onome obrađenom ovdje, a to je detekcija voznih traka. U usporedbi s tradicionalnim modelima plitke računske strukture, jedna vrlo važna prednost neuronskih mreža je ta što su reprezentacije podataka izgrađene u proces učenja automatski. Shodno tomu, neuronske mreže se smatraju sposobne za naučiti razlikovati sve značajke od interesa, naglašavajući da je ručna konstrukcija obilježja zamijenjena automatskim učenjem. Najčešće korištene mreže za detekciju cestovnih traka su konvolucijske neuronske mreže (CNN). U metodi za detekciju traka predstavljenoj ovdje se, koristeći duboko učenje, istrenirao model konvolucijske neuronske mreže tako da pronalazak lijeve i desne linije na cestovnih trakama bude robusniji i brži od modela koji se temelji na tradicionalnim algoritmima, odnosno na računalnom vidu. Dobiveni rezultati ukazuju na moć modela konvolucijskih neuronskih mreža koji je sposoban s velikom točnošću i malom funkcijom gubitka vrlo dobro odrediti trake i riješiti zadani problem.
Abstract (english)
Deep neural networks are a really powerful tool for visual analytics, for image interpretation and analysis, and have shown some really good performances in various tasks, including the one discussed here, which is lane detection. In comparison to traditional models with shallow computational structure, a very important adventage of neural networks is that the representations of the datasets are built in the process of learning automatically. Accordingly, neural networks are considered to be capable of learning to make a difference between all of the features of interest, emphasizing that the manual construction of these features is replaced by the autonomous learning. Most commonly used neural networks for lane detection are convolutional neural networks (CNN). In lane detection method proposed here, the CNN model was trained using deep learning, so that detecting of left and right line of the road lane is more robust and faster than from the model based on traditional algorithms. The results point out the power of CNN models that are capable of detecting lanes and solving problems with a big accuracy value and small loss function value.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:195213
Study programmeTitle: Electrical Engineering and Information Technology
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka elektrotehnike i informacijske tehnologije (magistar/magistra inženjer/inženjerka elektrotehnike i informacijske tehnologije)
Type of resourceText
File originBorn digital
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Created on2023-01-14 08:30:02