# A Dataset of Stationary, Fixed-wing Aircraft on a Collision Course for Vision-Based Sense and Avoid

arXiv: https://arxiv.org/abs/2112.02735 · HTML: https://mlsanity.com/paper/2112.02735/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 22 references that are in the map.
- First posted on arXiv 6 Dec 2021.

## Builds on (most cited first, 3 of 3)

- [Flying Objects Detection from a Single Moving Camera](https://mlsanity.com/paper/1411.7715/) (2014)
- [Below Horizon Aircraft Detection Using Deep Learning for Vision-Based Sense and Avoid](https://mlsanity.com/paper/1903.03275/) (2019)
- [Quickest Detection of Intermittent Signals With Application to Vision Based Aircraft Detection](https://mlsanity.com/paper/1804.09846/) (2018)

## Built on by (most cited first, 1 of 1)

- [TartanAviation: Image, Speech, and ADS-B Trajectory Datasets for Terminal Airspace Operations](https://mlsanity.com/paper/2403.03372/) (2024)

## Abstract (arXiv)

The emerging global market for unmanned aerial vehicle (UAV) services is anticipated to reach USD 58.4 billion by 2026, spurring significant efforts to safely integrate routine UAV operations into the national airspace in a manner that they do not compromise the existing safety levels. The commercial use of UAVs would be enhanced by an ability to sense and avoid potential mid-air collision threats however research in this field is hindered by the lack of available datasets as they are expensive and technically complex to capture. In this paper we present a dataset for vision based aircraft detection. The dataset consists of 15 image sequences containing 55,521 images of a fixed-wing aircraft approaching a stationary, grounded camera. Ground truth labels and a performance benchmark are also provided. To our knowledge, this is the first public dataset for studying medium sized, fixed-wing aircraft on a collision course with the observer. The full dataset and ground truth labels are publicly available at https://qcr.github.io/dataset/aircraft-collision-course/.