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.

通过研究环境和硬件条件引起的数据损坏对基于视觉的深度学习飞机检测和跟踪的影响，本文设计了七种常见的损坏类型，并构建了名为AOT-C的第一个用于飞行物体探测的健壮性基准数据集。基于实验评估结果，研究发现：YOLO家族的一阶段检测器表现出更好的健壮性，而基于Transformer和多阶段的检测器（如Faster R-CNN）对损坏非常敏感。此外，通过在合成数据上进行微调，可以改善目标检测器在真实飞行实验中的泛化能力。

增强和评估空对空视觉目标检测中的常见破坏