Modernized Training of U-Net for Aerial Semantic Segmentation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F24%3A43973190" target="_blank" >RIV/49777513:23520/24:43973190 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/10495686" target="_blank" >https://ieeexplore.ieee.org/document/10495686</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/WACVW60836.2024.00091" target="_blank" >10.1109/WACVW60836.2024.00091</a>
Alternative languages
Result language
angličtina
Original language name
Modernized Training of U-Net for Aerial Semantic Segmentation
Original language description
In this paper, we propose an improved training protocol of U-Net architecture for the semantic segmentation of aerial images. We test our approach on the challenging FLAIR #2 dataset. We present an extensive ablation study on the influence of different approach components on the overall performance. The ablation study includes a comparison of different model backbones, image augmentations, learning rate schedulers, loss functions, and training procedures. We additionally propose a two-stage training procedure and evaluate different options for the model ensemble. Based on the results we design the final setup of the model training protocol. This final setup decreases the relative error by approximately 18% and achieves mIoU equal to 0.641, which is a new state-of-the-art result. Our code is available at: https://github.com/strakaj/U-Net-for-remote-sensing
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
20205 - Automation and control systems
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)
ISBN
979-8-3503-7028-7
ISSN
2572-4398
e-ISSN
2690-621X
Number of pages
9
Pages from-to
785-793
Publisher name
Institute of Electrical and Electronics Engineers Inc.
Place of publication
Piscataway
Event location
Waikoloa
Event date
Jan 1, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001223022200092