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

TL;DR

This paper introduces a publicly available dataset of images capturing fixed-wing aircraft on a collision course, facilitating research in vision-based sense and avoid systems for UAV safety.

Contribution

The paper provides the first public dataset of medium-sized fixed-wing aircraft approaching a stationary camera, including ground truth labels and a performance benchmark.

Findings

Dataset contains 55,521 images of aircraft approaching a stationary camera.

Ground truth labels and benchmark performance data are provided.

Enables research in vision-based aircraft detection and collision avoidance.

Abstract

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/.