  Drone-based vehicle detection aims at finding the vehicle locations and
categories in an aerial image. It empowers smart city traffic management and
disaster rescue. Researchers have made mount of efforts in this area and
achieved considerable progress. Nevertheless, it is still a challenge when the
objects are hard to distinguish, especially in low light conditions. To tackle
this problem, we construct a large-scale drone-based RGB-Infrared vehicle
detection dataset, termed DroneVehicle. Our DroneVehicle collects 28, 439
RGB-Infrared image pairs, covering urban roads, residential areas, parking
lots, and other scenarios from day to night. Due to the great gap between RGB
and infrared images, cross-modal images provide both effective information and
redundant information. To address this dilemma, we further propose an
uncertainty-aware cross-modality vehicle detection (UA-CMDet) framework to
extract complementary information from cross-modal images, which can
significantly improve the detection performance in low light conditions. An
uncertainty-aware module (UAM) is designed to quantify the uncertainty weights
of each modality, which is calculated by the cross-modal Intersection over
Union (IoU) and the RGB illumination value. Furthermore, we design an
illumination-aware cross-modal non-maximum suppression algorithm to better
integrate the modal-specific information in the inference phase. Extensive
experiments on the DroneVehicle dataset demonstrate the flexibility and
effectiveness of the proposed method for crossmodality vehicle detection. The
dataset can be download from https://github.com/VisDrone/DroneVehicle.
