Lee, Jongseok und Olsman, WFJ und Triebel, Rudolph (2023) Learning Fluid Flow Visualizations From In-Flight Images With Tufts. IEEE Robotics and Automation Letters, 8 (6), Seiten 3677-3684. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/LRA.2023.3270746. ISSN 2377-3766.
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Offizielle URL: https://ieeexplore.ieee.org/abstract/document/10109020
Kurzfassung
To better understand fluid flows around aerial systems, strips of wire or rope, widely known as tufts, are often used to visualize the local flow direction. This letter presents a computer vision system that automatically extracts the shape of tufts from images, which have been collected during real flights of a helicopter and an unmanned aerial vehicle (UAV). As images from these aerial systems present challenges to both the model-based computer vision and the end-to-end supervised deep learning techniques, we propose a semantic segmentation pipeline that consists of three uncertainty-based modules namely, (a) active learning for object detection, (b) label propagation for object classification, and (c) weakly supervised instance segmentation. Overall, these probabilistic approaches facilitate the learning process without requiring any manual annotations of semantic segmentation masks. Empirically, we motivate our design choices through comparative assessments and provide real-world demonstrations of the proposed concept, for the first time to our knowledge.
elib-URL des Eintrags: | https://elib.dlr.de/195285/ | ||||||||||||||||
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Dokumentart: | Zeitschriftenbeitrag | ||||||||||||||||
Titel: | Learning Fluid Flow Visualizations From In-Flight Images With Tufts | ||||||||||||||||
Autoren: |
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Datum: | 26 April 2023 | ||||||||||||||||
Erschienen in: | IEEE Robotics and Automation Letters | ||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||
Open Access: | Ja | ||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||
In ISI Web of Science: | Ja | ||||||||||||||||
Band: | 8 | ||||||||||||||||
DOI: | 10.1109/LRA.2023.3270746 | ||||||||||||||||
Seitenbereich: | Seiten 3677-3684 | ||||||||||||||||
Verlag: | IEEE - Institute of Electrical and Electronics Engineers | ||||||||||||||||
ISSN: | 2377-3766 | ||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||
Stichwörter: | Aerial Systems: applications, computer vision for automation, object detection, segmentation and categorization, probability and statistical methods, aerodynamics | ||||||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||||||
HGF - Programm: | Raumfahrt | ||||||||||||||||
HGF - Programmthema: | Robotik | ||||||||||||||||
DLR - Schwerpunkt: | Raumfahrt | ||||||||||||||||
DLR - Forschungsgebiet: | R RO - Robotik | ||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | R - Intelligente Mobilität (RM) [RO], R - Erklärbare Robotische KI | ||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||
Institute & Einrichtungen: | Institut für Robotik und Mechatronik (ab 2013) Institut für Robotik und Mechatronik (ab 2013) > Perzeption und Kognition Institut für Aerodynamik und Strömungstechnik | ||||||||||||||||
Hinterlegt von: | Lee, Jongseok | ||||||||||||||||
Hinterlegt am: | 13 Jun 2023 11:54 | ||||||||||||||||
Letzte Änderung: | 13 Jun 2023 11:54 |
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