  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)
