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 this https URL

该研究构建了一个大规模的无人机 RGB - 红外车辆检测数据集，在低光条件下，使用不确定性感知的跨模态车辆检测框架提取交叉模态图像的补充信息，以大幅提高检测性能。

基于无人机的 RGB - 红外异模态车辆检测：基于不确定性感知的学习

Drone-based RGB-Infrared Cross-Modality Vehicle Detection via  Uncertainty-Aware Learning

We propose the concept of a multi-frame GAN (MFGAN) and demonstrate its
potential as an image sequence enhancement for stereo visual odometry in low
light conditions. We base our method on an invertible adversarial network to
transfer the beneficial features of brightly illuminated scenes to the sequence
in poor illumination without costly paired datasets. In order to preserve the
coherent geometric cues for the translated sequence, we present a novel network
architecture as well as a novel loss term combining temporal and stereo
consistencies based on optical flow estimation. We demonstrate that the
enhanced sequences improve the performance of state-of-the-art feature-based
and direct stereo visual odometry methods on both synthetic and real datasets
in challenging illumination. We also show that MFGAN outperforms other
state-of-the-art image enhancement and style transfer methods by a large margin
in terms of visual odometry.

本文提出了一种基于多帧 GAN 的图像序列增强方法，并通过实验验证了其在低光条件下用于立体视觉里程计的潜力。该方法不需要昂贵的成对数据集，通过可逆对抗网络的优秀性能，将亮度较高的场景中的有益特征传输到低照度序列中，并通过引入一个基于光流估计的时间和立体一致性的新型损失函数，保留了翻译序列的连贯几何线索。实验表明，该增强序列在具有挑战性的光照条件下，提高了最先进的基于特征和基于直接立体视觉里程计方法的性能，并且相比其他最先进的图像增强和风格转移方法，MFGAN 在视觉里程计方面表现出明显优势。