  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
