Publication
Title
Whitening transformation inspired self-attention for powerline element detection
Author
Abstract
Powerline inspection operations involve capturing and inspecting visual footage of powerline elements from elevated positions above and around the powerline and are currently performed with the help of helicopters and/or Unmanned Aerial Vehicles (UAVs). Current technological advances in the areas of robotics and machine learning are towards enabling fully autonomous operations. To this end, one of the tasks to be addressed is the robust, precise and fast powerline object detection problem. Recently introduced Transformer-based object detection methods demonstrate time and accuracy advances with respect to previous works. In this work, we present an enhanced Transformer-based architecture that further improves the state-of-the-art by incorporating a content-specific object query generator and by substituting the original attention operation with a whitening-inspired transformation at certain stages of the architecture. We evaluate our method in a recently captured powerline detection dataset and we show that our novel contributions offer a significant boost regarding detection accuracy.
Language
English
Source (journal)
Proceedings of the IAPR international conference on pattern recognition / IAPR International Conference on Pattern Recognition. - Los Alamitos
Source (book)
2022 26th International Conference on Pattern Recognition (ICPR), 21-25 August, 2022, Montreal, QC, Canada
Publication
New york : IEEE Computer Society Press , 2022
ISSN
1051-4651
ISBN
978-1-6654-9062-7
DOI
10.1109/ICPR56361.2022.9956307
Volume/pages
(2022) , p. 4844-4849
ISI
000897707604119
Full text (Publisher's DOI)
UAntwerpen
Publication type
Subject
External links
Web of Science
Record
Identifier
Creation 17.10.2023
Last edited 02.02.2024
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