The robustness of object detection models is a major concern when applied to real-world scenarios. However, the performance of most object detection models degrades when applied to images subjected to corruptions, since they are usually trained and evaluated on clean datasets. Enhancing the robustness of object detection models is of utmost importance, especially for those designed for aerial images, which feature complex backgrounds, substantial variations in scales and orientations of objects. This paper addresses the challenge of assessing the robustness of object detection models in aerial images, with a specific emphasis on scenarios where images are affected by clouds. In this study, we introduce two novel benchmarks based on DOTA-v1.0. The first benchmark encompasses 19 prevalent corruptions, while the second focuses on cloud-corrupted images-a phenomenon uncommon in natural pictures yet frequent in aerial photography. We systematically evaluate the robustness of mainstream object detection models and perform numerous ablation experiments. Through our investigations, we find that enhanced model architectures, larger networks, well-crafted modules, and judicious data augmentation strategies collectively enhance the robustness of aerial object detection models. The benchmarks we propose and our comprehensive experimental analyses can facilitate research on robust object detection in aerial images. Codes and datasets are available at: (https://github.com/hehaodong530/DOTA-C)

翻译：目标检测模型的鲁棒性是其应用于真实场景时面临的核心问题。然而，由于大多数目标检测模型通常基于干净数据集进行训练和评估，当应用于受噪声干扰的图像时，其性能会显著下降。提升目标检测模型的鲁棒性至关重要，尤其针对航拍影像设计的模型，这类图像具有背景复杂、目标尺度与方向变化显著的特点。本文旨在解决航拍影像中目标检测模型鲁棒性评估的挑战，重点关注图像受云层影响的具体场景。研究中，我们基于DOTA-v1.0数据集构建了两个新型基准测试集：第一个基准涵盖19种常见图像扰动，第二个则聚焦于云层干扰图像——这一现象在自然图像中罕见，但在航拍影像中频繁出现。我们系统评估了主流目标检测模型的鲁棒性，并开展了大量消融实验。通过研究，我们发现增强型模型架构、更大规模网络、精心设计的模块以及合理的数据增强策略，均能协同提升航拍目标检测模型的鲁棒性。本文提出的基准测试集及系统性实验分析，可促进航拍影像鲁棒目标检测的研究。代码与数据集访问地址：(https://github.com/hehaodong530/DOTA-C)