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master's thesis
Automatizirano ispravljanje slika iz dronova temeljeno na dubokom učenju
Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. urn:nbn:hr:168:043951
Horvat, Bruno
University of Zagreb
Faculty of Electrical Engineering and Computing

Institutional repository: FER Repository

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Horvat, B. (2023). Automatizirano ispravljanje slika iz dronova temeljeno na dubokom učenju (Master's thesis). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:043951

Horvat, Bruno. "Automatizirano ispravljanje slika iz dronova temeljeno na dubokom učenju." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:043951

Horvat, Bruno. "Automatizirano ispravljanje slika iz dronova temeljeno na dubokom učenju." Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, 2023. https://urn.nsk.hr/urn:nbn:hr:168:043951

Horvat, B. (2023). 'Automatizirano ispravljanje slika iz dronova temeljeno na dubokom učenju', Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 11 July 2024, https://urn.nsk.hr/urn:nbn:hr:168:043951

Horvat B. Automatizirano ispravljanje slika iz dronova temeljeno na dubokom učenju [Master's thesis]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2023 [cited 2024 July 11] Available at: https://urn.nsk.hr/urn:nbn:hr:168:043951

B. Horvat, "Automatizirano ispravljanje slika iz dronova temeljeno na dubokom učenju", Master's thesis, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:168:043951

Metadata
TitleAutomatizirano ispravljanje slika iz dronova temeljeno na dubokom učenju
Title (english)Deep Learning for Automatized Correction of Images Taken from Drones
AuthorBruno Horvat
MentorVladimir Čeperić (mentor)
Committee memberVladimir Čeperić (predsjednik povjerenstva)
Committee memberZoran Kalafatić (član povjerenstva)
Committee memberTvrtko Mandić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
Zagreb
Defense date and country2023-07-13, Croatia
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Computing
Abstract
Ovim radom isprobane su razne metode učenja rekonstruiranja slike pomoću neuronske mreže. Najprije je dana teorijska podloga raznih vrsta modela te je opisano kako rade i koje su njihove prednosti. Opisane metode su služile kao kratak uvod u područje dubokog učenja. Bez prevelikog ulaženja u detalje, opisane su one metode koje će u ovom radu biti i korištene. Za treniranje neuronske mreže je potreban skup podataka koji je raznim augmentacijama pokušao simulirati nepovoljne vremenske uvjete. Problem čišćenja slika je prikazan kroz analitičko razmišljanje te je napravljena usporedba modela i njihovih rezultata. Svaki model sastoji se od enkoderske i dekoderske mreže koje zajedničkim snagama pokušavaju rekonstruirati sliku iz drona. Modeli dubokog učenja naučeni su najviše što su mogli biti s obzirom na hardverske resurse.
Abstract (english)
The topic of the following paper is testing various methods of image reconstruction using a neural network. Firstly, a theoretical background of different types of models is provided, describing how they work and what are their advantages. The described methods served as a brief introduction to the field of deep learning. Without going into too much details, only the methods that are used in this work were described. To train the neural network, a dataset was required, which was subjected to various augmentations in order to simulate weather conditions. The problem of image denoising has been shown through analytical thinking, and a comparison of models and their results was conducted. Each model consists of an encoder and a decoder network, which work together to reconstruct images captured by a drone. The deep learning models were trained to the best of their ability given the available hardware resources.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:168:043951
Study programmeTitle: Computing
Study programme type: university
Study level: graduate
Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
Type of resourceText
File originBorn digital
Access conditionsClosed access
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Created on2023-10-11 09:02:20