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Evaluation of Deep Learning-Based Neural Network Methods for Cloud Detection and Segmentation

  • This paper presents a systematic approach for accurate short-time cloud coverage prediction based on a machine learning (ML) approach. Based on a newly built omnidirectional ground-based sky camera system, local training and evaluation data sets were created. These were used to train several state-of-the-art deep neural networks for object detection and segmentation. For this purpose, theThis paper presents a systematic approach for accurate short-time cloud coverage prediction based on a machine learning (ML) approach. Based on a newly built omnidirectional ground-based sky camera system, local training and evaluation data sets were created. These were used to train several state-of-the-art deep neural networks for object detection and segmentation. For this purpose, the camera-generated a full hemispherical image every 30 min over two months in daylight conditions with a fish-eye lens. From this data set, a subset of images was selected for training and evaluation according to various criteria. Deep neural networks, based on the two-stage R-CNN architecture, were trained and compared with a U-net segmentation approach implemented by CloudSegNet. All chosen deep networks were then evaluated and compared according to the local situation.show moreshow less

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Metadaten
Document Type:Article (reviewed)
Zitierlink: https://opus.hs-offenburg.de/5320
Bibliografische Angaben
Title (English):Evaluation of Deep Learning-Based Neural Network Methods for Cloud Detection and Segmentation
Author:Stefan HenselStaff MemberORCiDGND, Marin B. Marinov, Michael Koch, Dimitar Arnaudov
Date of Publication (online):2021/09/27
Place of publication:Basel
Publisher:MDPI
Page Number:14
First Page:1
Last Page:14
Article Number:6156
Parent Title (English):Energies
Editor:Boštjan Blažič
Volume:14
Issue:19
ISSN:1996-1073
DOI:https://doi.org/10.3390/en14196156
URN:https://urn:nbn:de:bsz:ofb1-opus4-53201
Language:English
Inhaltliche Informationen
Institutes:Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019)
Institutes:Bibliografie
DDC classes:600 Technik, Medizin, angewandte Wissenschaften
Formale Angaben
Open Access: Open Access 
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International