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:
(this https URL)

在本文中，我们针对受云层影响的航拍图像，提出了两个基于 DOTA-v1.0 的新型基准测试。通过对主流目标检测模型的系统评估和大量剖析实验，我们发现增强模型架构、更大的网络、精心设计的模块以及谨慎的数据增强策略可以共同提高航拍目标检测模型的稳健性。我们提出的基准测试和全面的实验分析有助于促进航拍图像中稳健目标检测的研究。