Comparative Analysis of Modern Methods for Surface Type Identification in RGB Image Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F24%3A39921880" target="_blank" >RIV/00216275:25410/24:39921880 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.webofscience.com/wos/woscc/full-record/WOS:001266171500008" target="_blank" >https://www.webofscience.com/wos/woscc/full-record/WOS:001266171500008</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ZINC61849.2024.10579297" target="_blank" >10.1109/ZINC61849.2024.10579297</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparative Analysis of Modern Methods for Surface Type Identification in RGB Image Data

Popis výsledku v původním jazyce

The rapid development of drone technology and deep learning algorithms also expands the possibilities of environmental monitoring, e.g., the search and management of water bodies. This study aims to harness these advances for the accurate identification of surface types, with a specific focus on water bodies, in RGB image data. Using a dataset comprised of aerial images captured over the Baroch Pond within a nature reserve in the Czech Republic, this study comparative to evaluate the performance of deep learning models, including U-Net, Pyramid Scene Parsing Network (PSPNet), and DeepLabV3, in classifying surface types. The classification accuracy is slightly over 90% for most deep learning algorithms. These results show the potential of deep learning in this area. And this is key for a number of interested parties, for example, state administration, water resource managers, farmers, and tourism industry.

Název v anglickém jazyce

Comparative Analysis of Modern Methods for Surface Type Identification in RGB Image Data

Popis výsledku anglicky

The rapid development of drone technology and deep learning algorithms also expands the possibilities of environmental monitoring, e.g., the search and management of water bodies. This study aims to harness these advances for the accurate identification of surface types, with a specific focus on water bodies, in RGB image data. Using a dataset comprised of aerial images captured over the Baroch Pond within a nature reserve in the Czech Republic, this study comparative to evaluate the performance of deep learning models, including U-Net, Pyramid Scene Parsing Network (PSPNet), and DeepLabV3, in classifying surface types. The classification accuracy is slightly over 90% for most deep learning algorithms. These results show the potential of deep learning in this area. And this is key for a number of interested parties, for example, state administration, water resource managers, farmers, and tourism industry.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2024 Zooming Innovation in Consumer Technologies Conference (ZINC)

ISBN

979-8-3503-4916-0

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

84-89

Název nakladatele

IEEE (Institute of Electrical and Electronics Engineers)

Místo vydání

New York

Místo konání akce

Novi Sad

Datum konání akce

22. 5. 2024

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

001266171500008