Vehicle detection and recognition in drone images is a complex problem that has been used for different safety purposes. The main challenge of these images is captured at oblique angles and poses several challenges like non-uniform illumination effect, degradations, blur, occlusion, loss of visibility, etc. Additionally, weather conditions play a crucial role in causing safety concerns and add another high level of challenge to the collected data. Over the past few decades, various techniques have been employed to detect and track vehicles in different weather conditions. However, detecting vehicles in heavy snow is still in the early stages because of a lack of available data. Furthermore, there has been no research on detecting vehicles in snowy weather using real images captured by unmanned aerial vehicles (UAVs). This study aims to address this gap by providing the scientific community with data on vehicles captured by UAVs in different settings and under various snow cover conditions in the Nordic region. The data covers different adverse weather conditions like overcast with snowfall, low light and low contrast conditions with patchy snow cover, high brightness, sunlight, fresh snow, and the temperature reaching far below -0 degrees Celsius. The study also evaluates the performance of commonly used object detection methods such as Yolo v8, Yolo v5, and fast RCNN. Additionally, data augmentation techniques are explored, and those that enhance the detectors' performance in such scenarios are proposed. The code and the dataset will be available at https://nvd.ltu-ai.dev

翻译：无人机图像中的车辆检测与识别是一个复杂问题，已应用于多种安全场景。这类图像的主要挑战在于其倾斜拍摄角度，导致非均匀光照效应、退化、模糊、遮挡、能见度丧失等问题。此外，天气条件在引发安全担忧方面起着关键作用，并为采集数据增加了更高层次的挑战。过去数十年间，研究人员已采用多种技术在各类天气条件下检测与追踪车辆。然而，由于可用数据匮乏，在大雪天气下的车辆检测仍处于早期阶段。更进一步，目前尚无研究使用无人机（UAV）在雪天条件下拍摄的真实图像进行车辆检测。本研究旨在填补这一空白，通过向科学界提供北欧地区不同场景及不同积雪覆盖条件下无人机捕获的车辆数据。该数据涵盖了多种不利天气条件，如阴天伴随降雪、低光低对比度下出现斑块状积雪、高亮度、阳光照射、新雪覆盖以及远低于-0摄氏度的温度。研究还评估了Yolo v8、Yolo v5和Fast RCNN等常用目标检测方法的性能。同时，探索了数据增强技术，并提出了在此类场景中能提升检测器性能的方法。代码和数据集将发布于https://nvd.ltu-ai.dev