Learning Fluid Flow Visualizations From In-Flight Images With Tufts

摘要

<p><strong>Abstract:</strong> 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.</p> <p><strong>MetaInfo: </strong>This dataset accompanies the publication: Learning Fluid Flow Visualizations From In-Flight Images With Tufts at IEEE RA-L 2023. The data consists of images and annotations of tufts for fluid flow visualization, which were used to validate a semantic segmentation method based on uncertainty in deep learning.</p> <p>Images of tufts from the DLR helicopter EC135-ACT⁄FHS have been collected at the Institute of Aerodynamics and Flow Technology. Images of tufts from the stratospheric flight of the robot HABLEG have been collected at the Institute of Robotics and Mechatronics.</p> <p><strong>Project website:</strong> https://sites.google.com/view/tuftrecognition/</p> <p>This website contains more details about the data collection procedures, and how tufts have been placed on the considered aerial vehicles.</p> <p><strong>Bibtex:</strong> This dataset can be cited as the same way of the original article.</p> <blockquote> <pre>@ARTICLE{10109020, author={Lee, Jongseok and Olsman, W.F.J. and Triebel, Rudolph}, journal={IEEE Robotics and Automation Letters}, title={Learning Fluid Flow Visualizations From In-Flight Images With Tufts}, year={2023}, volume={8}, number={6}, pages={3677-3684}, doi={10.1109/LRA.2023.3270746}} </pre> </blockquote>