Deep Learning Method for Heliostat Instance Segmentation
Abstract: Heliostat instance segmentation (HST-IS) is a crucial component of the heliostat tracking system at Heliogen’s Lancaster test facility. The system estimates the mirror normal of each heliostat by performing a nonlinear optimization-based fitting strategy using approximations of the non-shaded, non-blocked sunlit pixels on each heliostat, and the tracking system uses these estimates to improve performance. HST-IS is fundamentally challenging due to variability in lighting conditions and heliostat size relative to the capturing camera. Deep learning-based convolutional neural networks (CNN) have emerged in recent years by demonstrating noteworthy precision in tasks such as object recognition, detection, and segmentation. CNN-based methods offer a robust augmentation to HST-IS methods as they capture a context-less hierarchy of image features. In this study, we developed deep learning models to automatically segment heliostat instances from elevated images taken from the field. We stu.... https://www.tib-op.org/ojs/index.php/solarpaces/article/view/735
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Deep Learning Method for Heliostat Instance Segmentation ; volume:1 ; year:2022
SolarPACES conference proceedings ; 1 (2022)
- Creator
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Liu, Benjamin
Sonn, Alexander
Roy, Anthony
Brewington, Brian
- DOI
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10.52825/solarpaces.v1i.735
- URN
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urn:nbn:de:101:1-2024032617400690247588
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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14.08.2025, 10:47 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Liu, Benjamin
- Sonn, Alexander
- Roy, Anthony
- Brewington, Brian
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