Challenging YOLO and Faster RCNN in Snowy Conditions: UAV Nordic Vehicle Dataset (NVD) as an Example

In the world of autonomous systems and aerial surveillance, the quest to efficiently detect vehicles in diverse environmental conditions has emerged as a pivotal challenge. While these technologies have made significant advancements in the identification of objects under ordinary circumstances, the complexities introduced by snow-laden landscapes present a unique set of hurdles. The deployment of unmanned aerial vehicles (UAVs) equipped with state-of-the-art detectors in snowy regions has become an area of intense research, as it holds promise for various applications, from search and rescue operations to efficient transportation management. This paper explores the complexities that surface when it comes to identifying vehicles within snowy landscapes through the utilization of drones. It delves into the intricacies of this state-ofthe-art undertaking, offering insights into potential future directions to tackle these challenges for the unique demands of such environments. The research aims to apply the conventional procedures typically used to enhance the performance of stateof-the-art (STOA) detectors such as YOLO and faster RCNN. This is done to underscore that adhering to traditional approaches may not suffice to achieve the desired level of efficiency and accuracy when viewed from an industrial standpoint. The code and the dataset will be available at https://nvd.ltu-ai.dev/