  The development of multi-modal object detection for Unmanned Aerial Vehicles
(UAVs) typically relies on a large amount of pixel-aligned multi-modal image
data. However, existing datasets face challenges such as limited modalities,
high construction costs, and imprecise annotations. To this end, we propose a
synthetic multi-modal UAV-based object detection dataset, UEMM-Air. Specially,
we simulate various UAV flight scenarios and object types using the Unreal
Engine (UE). Then we design the UAV's flight logic to automatically collect
data from different scenarios, perspectives, and altitudes. Finally, we propose
a novel heuristic automatic annotation algorithm to generate accurate object
detection labels. In total, our UEMM-Air consists of 20k pairs of images with 5
modalities and precise annotations. Moreover, we conduct numerous experiments
and establish new benchmark results on our dataset. We found that models
pre-trained on UEMM-Air exhibit better performance on downstream tasks compared
to other similar datasets. The dataset is publicly available
(https://github.com/1e12Leon/UEMM-Air) to support the research of multi-modal
UAV object detection models.
