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Title:Razvrščanje odpadkov s pomočjo globokih nevronskih mrež
Authors:ID Grneva, Teodora (Author)
ID Verber, Domen (Mentor) More about this mentor... New window
Files:.pdf MAG_Grneva_Teodora_2025.pdf (1,67 MB)
MD5: 689D0DBC6DC3F62F68705C5A81738DCA
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Magistrsko delo se osredotoča na pomemben izziv učinkovitega razvrščanja odpadkov. Pravilna identifikacija in ločevanje odpadkov sta ključna za izboljšano ravnanje z njimi, višje stopnje recikliranja in zmanjšanje negativnih vplivov na okolje. V nalogi uporabljamo napredne tehnike globokega učenja, predvsem kompleksne umetne nevronske mreže, za natančno klasifikacijo odpadkov na podlagi slik. Cilj je optimizirati razvrščanje odpadkov z raziskovanjem in primerjavo različnih modelov globokega učenja, tehnik predobdelave slik ter prenosom znanja za izboljšanje natančnosti klasifikacije. Ugotovitve bodo prispevale k razvoju naprednih sistemov za ravnanje z odpadki in ohranjanju okolja.
Keywords:strojno učenje, globoko učenje, klasifikacija slik, razvrščanje odpadkov, nevronske mreže
Place of publishing:Maribor
Publisher:[T. Grneva]
Year of publishing:2025
PID:20.500.12556/DKUM-93619 New window
UDC:004.85:628.4.08(043.2)
COBISS.SI-ID:254000899 New window
Publication date in DKUM:04.09.2025
Views:385
Downloads:51
Metadata:XML DC-XML DC-RDF
Categories:KTFMB - FERI
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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:08.07.2025

Secondary language

Language:English
Title:Waste sorting using deep neural networks
Abstract:The master's thesis focuses on the significant challenge of effective waste sorting. Accurate identification and separation of waste are essential for improved waste management, higher recycling rates, and reducing negative environmental impacts. This study employs advanced deep learning techniques, particularly complex artificial neural networks, for precise waste classification based on images. The goal is to optimize waste sorting by exploring and comparing various deep learning models, image preprocessing techniques, and knowledge transfer to enhance classification accuracy. The findings will contribute to the development of advanced waste management systems and environmental conservation.
Keywords:machine learning, deep learning, image classification, waste sorting, neural networks


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