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Title:Iskanje številke osnovnega sredstva iz fotografije izolatorja s pomočjo strojnega učenja in geografske lokacije : magistrska naloga
Authors:ID Trunkl, Andrej (Author)
ID Boškoski, Pavle (Mentor) More about this mentor... New window
Files:.pdf MAG_2024_Andrej_Trunkl.pdf (3,01 MB)
MD5: CF9E63D11D10CA480EB18DD5B251A96F
 
Language:Slovenian
Work type:Master's thesis/paper
Typology:2.09 - Master's Thesis
Organization:FIŠ - Faculty of Information Studies in Novo mesto
Abstract:cELES, družba za upravljanje z elektroenergetskim prenosnim sistemom, skrbi za nemoteno delovanje slovenskega električnega omrežja. Za zagotovitev nemotenega delovanja je treba izvajati redne preglede sredstev na omrežju. Ker so pregledi zamudni, se uporabljajo brezpilotna letala, s katerimi se fotografirajo sredstva na omrežjih. Količina tako posnetih fotografij je zelo velika, zato je ročno pregledovanje in prostorsko umeščanje teh posnetkov velik izziv. Cilj raziskave je izboljšati učinkovitost spremljanja stanja omrežja z razvojem metod za natančnejše in hitrejše zaznavanje ter analizo infrastrukture, kar bo izboljšalo zanesljivost in varnost omrežja.
Keywords:prostorsko umeščanje, zaznavanje infrastrukture, pred procesiranje fotografij, predpripravljeni modeli, prenos znanja
Place of publishing:Novo mesto
Place of performance:Novo mesto
Publisher:A. Trunkl
Year of publishing:2024
Year of performance:2024
Number of pages:XV, 83 str.
PID:20.500.12556/ReVIS-11348 New window
COBISS.SI-ID:223132675 New window
UDC:004.92:004.85(043.2)
Note:Na ov.: Magistrska naloga : študijskega programa druge stopnje;
Publication date in ReVIS:20.01.2025
Views:75
Downloads:9
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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.

Secondary language

Language:English
Abstract:ELES, the company responsible for managing the electricity transmission system, ensures the smooth operation of Slovenia’s electrical grid. To maintain uninterrupted service, regular inspections of network assets are necessary. Due to the time-consuming nature of these inspections, drones are used to photograph the network assets. The large volume of images captured presents a significant challenge for manual review and spatial placement. The aim of this research is to enhance the efficiency of network monitoring by developing methods for more accurate and faster detection and analysis of infrastructure, thereby improving the reliability and safety of the grid.
Keywords:Spatial Placement, infrastructure detection, image preprocessing, Pre-trained Models, transfer learning


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