Detecting humans from airborne visual and thermal imagery is a fundamental
challenge for Wilderness Search-and-Rescue (WiSAR) teams, who must perform this
function accurately in the face of immense pressure. The ability to fuse these
two sensor modalities can potentially reduce the cognitive load on human
operators and/or improve the effectiveness of computer vision object detection
models. However, the fusion task is particularly challenging in the context of
WiSAR due to hardware limitations and extreme environmental factors. This work
presents Misaligned Image Synthesis and Fusion using Information from Thermal
and Visual (MISFIT-V), a novel two-pronged unsupervised deep learning approach
that utilizes a Generative Adversarial Network (GAN) and a cross-attention
mechanism to capture the most relevant features from each modality.
Experimental results show MISFIT-V offers enhanced robustness against
misalignment and poor lighting/thermal environmental conditions compared to
existing visual-thermal image fusion methods.

使用来自热像和视觉的信息进行失配图像合成和融合（MISFIT-V）是一种新型的双重无监督深度学习方法，利用生成对抗网络（GAN）和交叉注意机制从每个模态中捕获最相关的特征。实验结果表明，与现有的视觉 - 热像融合方法相比，MISFIT-V 在失配和光照 / 热环境条件较差的情况下具有增强的鲁棒性。

MISFIT-V：利用热像和可视信息的错位图像合成与融合

MISFIT-V: Misaligned Image Synthesis and Fusion using Information from  Thermal and Visual

Multi-class vehicle detection from airborne imagery with orientation
estimation is an important task in the near and remote vision domains with
applications in traffic monitoring and disaster management. In the last decade,
we have witnessed significant progress in object detection in ground imagery,
but it is still in its infancy in airborne imagery, mostly due to the scarcity
of diverse and large-scale datasets. Despite being a useful tool for different
applications, current airborne datasets only partially reflect the challenges
of real-world scenarios. To address this issue, we introduce EAGLE (oriEnted
vehicle detection using Aerial imaGery in real-worLd scEnarios), a large-scale
dataset for multi-class vehicle detection with object orientation information
in aerial imagery. It features high-resolution aerial images composed of
different real-world situations with a wide variety of camera sensor,
resolution, flight altitude, weather, illumination, haze, shadow, time, city,
country, occlusion, and camera angle. The annotation was done by airborne
imagery experts with small- and large-vehicle classes. EAGLE contains 215,986
instances annotated with oriented bounding boxes defined by four points and
orientation, making it by far the largest dataset to date in this task. It also
supports researches on the haze and shadow removal as well as super-resolution
and in-painting applications. We define three tasks: detection by (1)
horizontal bounding boxes, (2) rotated bounding boxes, and (3) oriented
bounding boxes. We carried out several experiments to evaluate several
state-of-the-art methods in object detection on our dataset to form a baseline.
Experiments show that the EAGLE dataset accurately reflects real-world
situations and correspondingly challenging applications.

EAGLE 是一种大规模的数据集，用于航空图像中多类车辆的检测，并包含了各种各样的真实世界情况，由定向边界框定义并注释，支持研究雾霾和阴影去除以及超分辨率和修补应用。