Vehicle Detection Performance in Nordic Region
Hamam Mokayed Rajkumar Saini Oluwatosin Adewumi Lama Alkhaled Bjorn Backe Palaiahnakote Shivakumara Olle Hagner Yan Chai Hum
This paper addresses the critical challenge of vehicle detection in the harsh
winter conditions in the Nordic regions, characterized by heavy snowfall,
reduced visibility, and low lighting. Due to their susceptibility to
environmental distortions and occlusions, traditional vehicle detection methods
have struggled in these adverse conditions. The advanced proposed deep learning
architectures brought promise, yet the unique difficulties of detecting
vehicles in Nordic winters remain inadequately addressed. This study uses the
Nordic Vehicle Dataset (NVD), which has UAV images from northern Sweden, to
evaluate the performance of state-of-the-art vehicle detection algorithms under
challenging weather conditions. Our methodology includes a comprehensive
evaluation of single-stage, two-stage, and transformer-based detectors against
the NVD. We propose a series of enhancements tailored to each detection
framework, including data augmentation, hyperparameter tuning, transfer
learning, and novel strategies designed explicitly for the DETR model. Our
findings not only highlight the limitations of current detection systems in the
Nordic environment but also offer promising directions for enhancing these
algorithms for improved robustness and accuracy in vehicle detection amidst the
complexities of winter landscapes. The code and the dataset are available at
https://nvd.ltu-ai.dev