Comparison of Deep Learning and Object-Based Image Classification Methods: Identification of Horses from RGB Imagery
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F25%3A39921812" target="_blank" >RIV/00216275:25410/25:39921812 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S1877050925020769" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1877050925020769</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.procs.2025.07.026" target="_blank" >10.1016/j.procs.2025.07.026</a>
Alternative languages
Result language
angličtina
Original language name
Comparison of Deep Learning and Object-Based Image Classification Methods: Identification of Horses from RGB Imagery
Original language description
The paper describes the utilisation of remotely sensed RGB data to support routine monitoring of horses in a natural environment on demand. Data are sensed using an unmanned aerial vehicle (UAV). UAVs provide very high spatial resolution data sensed at a low altitude on demand. Sensing is limited by weather conditions and legal rules only. Terrain does not need to be accessible. The article provides a comparison of several classification methods, namely object-based classification methods and Deep Learning classification. Namely Maximum Likelihood, Random Trees, Support Vector Machine (SVM), K-Nearest Neighbour (K-NN) and Deep Learning models U-Net and Deep Lab version 3. Manual classification is used as the reference method.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10200 - Computer and information sciences
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Procedia Computer Science: International Conference on Industry Sciences and Computer Science Innovation (iSCSi’24)
ISBN
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ISSN
1877-0509
e-ISSN
1877-0509
Number of pages
8
Pages from-to
208-215
Publisher name
Elsevier B.V.
Place of publication
Amsterdam
Event location
Porto
Event date
Oct 29, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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