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Learning Fluid Flow Visualizations From In-Flight Images With Tufts

Lee, Jongseok and Olsman, WFJ and Triebel, Rudolph (2023) Learning Fluid Flow Visualizations From In-Flight Images With Tufts. IEEE Robotics and Automation Letters, 8 (6), pp. 3677-3684. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/LRA.2023.3270746. ISSN 2377-3766.

[img] PDF - Postprint version (accepted manuscript)

Official URL: https://ieeexplore.ieee.org/abstract/document/10109020


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.

Item URL in elib:https://elib.dlr.de/195285/
Document Type:Article
Title:Learning Fluid Flow Visualizations From In-Flight Images With Tufts
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Lee, JongseokUNSPECIFIEDhttps://orcid.org/0000-0002-0960-0809UNSPECIFIED
Triebel, RudolphUNSPECIFIEDhttps://orcid.org/0000-0002-7975-036XUNSPECIFIED
Date:26 April 2023
Journal or Publication Title:IEEE Robotics and Automation Letters
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
Page Range:pp. 3677-3684
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Keywords:Aerial Systems: applications, computer vision for automation, object detection, segmentation and categorization, probability and statistical methods, aerodynamics
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Robotics
DLR - Research area:Raumfahrt
DLR - Program:R RO - Robotics
DLR - Research theme (Project):R - Intelligent Mobility (RM) [RO], R - Explainable Robotic AI
Location: Oberpfaffenhofen
Institutes and Institutions:Institute of Robotics and Mechatronics (since 2013)
Institute of Robotics and Mechatronics (since 2013) > Perception and Cognition
Institute for Aerodynamics and Flow Technology
Deposited By: Lee, Jongseok
Deposited On:13 Jun 2023 11:54
Last Modified:13 Jun 2023 11:54

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