AVOIDDS: A dataset for vision-based aircraft detection
Abstract/Contents
- Abstract
- Aircraft collision avoidance systems rely on sensor information to detect and track intruding aircraft so that they may issue proper collision avoidance advisories. While typical surveillance sensors for manned aircraft include transponders and onboard radar, autonomous aircraft will require additional sensors both for redundancy and to replace the visual acquisition typically performed by the pilot. As a result, the community has proposed detecting other aircraft using vision-based sensors such as cameras. These sensors require the development of techniques to process images of the environment to detect intruding aircraft. To boost this development, this artifact provides a dataset of 72,000 labeled images of intruder aircraft with various lighting conditions, weather conditions, relative geometries, and geographic locations. For more information on the structure of this dataset as well as benchmark models and a full simulator, see https://github.com/sisl/VisionBasedAircraftDAA.
Description
| Type of resource | Dataset, still image |
|---|---|
| Publication date | May 25, 2023 |
Creators/Contributors
| Contributor | Smyers, Elysia | |
|---|---|---|
| Contributor | Katz, Sydney |
https://orcid.org/0000-0001-8376-5145
(unverified)
|
| Researcher | Corso, Anthony |
https://orcid.org/0000-0002-4027-0473
(unverified)
|
| Research team head | Kochenderfer, Mykel |
https://orcid.org/0000-0002-7238-9663
(unverified)
|
Subjects
| Subject | Machine learning |
|---|---|
| Subject | Object Detection |
| Subject | Aviation |
| Genre | Data |
| Genre | Image |
| Genre | Data sets |
| Genre | Dataset |
Bibliographic information
| Related item | |
|---|---|
| DOI | https://doi.org/10.25740/hj293cv5980 |
| Location | https://purl.stanford.edu/hj293cv5980 |
Access conditions
- Use and reproduction
- User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
- License
- This work is licensed under a Creative Commons Attribution 4.0 International license (CC BY).
Preferred citation
- Preferred citation
- Smyers, E., Katz, S., Corso, A., and Kochenderfer, M. (2023). AVOIDDS: A dataset for vision-based aircraft detection. Stanford Digital Repository. Available at https://purl.stanford.edu/hj293cv5980. https://doi.org/10.25740/hj293cv5980.
Collection
Stanford Research Data
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