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

Common Corruptions for Enhancing and Evaluating Robustness in Air-to-Air  Visual Object Detection

We present a micro aerial vehicle (MAV) system, built with inexpensive
off-the-shelf hardware, for autonomously following trails in unstructured,
outdoor environments such as forests. The system introduces a deep neural
network (DNN) called TrailNet for estimating the view orientation and lateral
offset of the MAV with respect to the trail center. The DNN-based controller
achieves stable flight without oscillations by avoiding overconfident behavior
through a loss function that includes both label smoothing and entropy reward.
In addition to the TrailNet DNN, the system also utilizes vision modules for
environmental awareness, including another DNN for object detection and a
visual odometry component for estimating depth for the purpose of low-level
obstacle detection. All vision systems run in real time on board the MAV via a
Jetson TX1. We provide details on the hardware and software used, as well as
implementation details. We present experiments showing the ability of our
system to navigate forest trails more robustly than previous techniques,
including autonomous flights of 1 km.

该研究提出了一种使用廉价的现成硬件构建的微型飞行器系统，其利用名为 TrailNet 的深度神经网络自主跟踪树林等户外环境中的小路，结合视觉感知技术，包括对象检测和视觉里程计模块，以实现更加稳定和准确的飞行。