Learning to Detect Aircraft for Long-Range Vision-Based Sense-and-Avoid Systems

Abstract

The commercial use of unmanned aerial vehicles (UAVs) would be enhanced by an ability to sense and avoid potential mid-air collision threats. In this letter, we propose a new approach to aircraft detection for long-range vision-based sense and avoid. We first train a deep convolutional neural network to learn aircraft visual features using flight data of mid-air head-on near-collision course encounters between two fixed-wing aircraft. We then propose an approach that fuses these learnt aircraft features with hand-crafted features that are used by the current state of the art. Finally, we evaluate the performance of our proposed approach on real flight data captured from a UAV, where it achieves a mean detection range of 2527 m and a mean detection range improvement of 299 m (or 13.4%) compared to the current state of the art with no additional false alarms.