DDOS: The drone depth and obstacle segmentation dataset
Author(s)
Kolbeinsson, Benedikt
Mikolajczyk, Krystian
Type
Conference Paper
Abstract
The advancement of autonomous drones, essential for sectors such as remote sensing and emergency services, is hindered by the absence of training datasets that fully capture the environmental challenges present in real-world scenarios, particularly operations in non-optimal weather conditions and the detection of thin structures like wires. We present the Drone Depth and Obstacle Segmentation (DDOS) dataset to fill this critical gap with a collection of synthetic aerial images, created to provide comprehensive training samples for semantic segmentation and depth estimation. Specifically designed to enhance the identification of thin structures, DDOS allows drones to navigate a wide range of weather conditions, significantly elevating drone training and operational safety. Additionally, this work introduces innovative drone-specific metrics aimed at refining the evaluation of algorithms in depth estimation, with a focus on thin structure detection. These contributions not only pave the way for substantial improvements in autonomous drone technology but also set a new benchmark for future research, opening avenues for further advancements in drone navigation and safety.
Date Issued
2024-09-27
Date Acceptance
2024-06-01
Citation
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2024, pp.7328-7337
ISSN
2160-7508
Publisher
IEEE Computer Society
Start Page
7328
End Page
7337
Journal / Book Title
2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Copyright Statement
Copyright © 2024, IEEE. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Source
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Subjects
AIRCRAFT
Computer Science
Computer Science, Artificial Intelligence
Computer Science, Interdisciplinary Applications
Science & Technology
Technology
UAVS
Publication Status
Published
Start Date
2024-06-16
Finish Date
2024-06-22
Coverage Spatial
Seattle, WA, USA