  Detecting objects from Unmanned Aerial Vehicles (UAV) is often hindered by a
large number of small objects, resulting in low detection accuracy. To address
this issue, mainstream approaches typically utilize multi-stage inferences.
Despite their remarkable detecting accuracies, real-time efficiency is
sacrificed, making them less practical to handle real applications. To this
end, we propose to improve the single-stage inference accuracy through learning
scale-invariant features. Specifically, a Scale-Invariant Feature Disentangling
module is designed to disentangle scale-related and scale-invariant features.
Then an Adversarial Feature Learning scheme is employed to enhance
disentanglement. Finally, scale-invariant features are leveraged for robust
UAV-based object detection. Furthermore, we construct a multi-modal UAV object
detection dataset, State-Air, which incorporates annotated UAV state
parameters. We apply our approach to three state-of-the-art lightweight
detection frameworks on three benchmark datasets, including State-Air.
Extensive experiments demonstrate that our approach can effectively improve
model accuracy. Our code and dataset are provided in Supplementary Materials
and will be publicly available once the paper is accepted.
