  The main barrier to achieving fully autonomous flights lies in autonomous
aircraft navigation. Managing non-cooperative traffic presents the most
important challenge in this problem. The most efficient strategy for handling
non-cooperative traffic is based on monocular video processing through deep
learning models. This study contributes to the vision-based deep learning
aircraft detection and tracking literature by investigating the impact of data
corruption arising from environmental and hardware conditions on the
effectiveness of these methods. More specifically, we designed $7$ types of
common corruptions for camera inputs taking into account real-world flight
conditions. By applying these corruptions to the Airborne Object Tracking (AOT)
dataset we constructed the first robustness benchmark dataset named AOT-C for
air-to-air aerial object detection. The corruptions included in this dataset
cover a wide range of challenging conditions such as adverse weather and sensor
noise. The second main contribution of this letter is to present an extensive
experimental evaluation involving $8$ diverse object detectors to explore the
degradation in the performance under escalating levels of corruptions (domain
shifts). Based on the evaluation results, the key observations that emerge are
the following: 1) One-stage detectors of the YOLO family demonstrate better
robustness, 2) Transformer-based and multi-stage detectors like Faster R-CNN
are extremely vulnerable to corruptions, 3) Robustness against corruptions is
related to the generalization ability of models. The third main contribution is
to present that finetuning on our augmented synthetic data results in
improvements in the generalisation ability of the object detector in real-world
flight experiments.
