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

翻译：本文探讨了在斯堪的纳维亚地区严冬条件下车辆检测所面临的关键挑战，这些地区具有强降雪、低能见度和光照不足等特点。由于传统车辆检测方法易受环境干扰和遮挡影响，在恶劣条件下表现欠佳。虽然先进的深度学习架构带来了一定突破，但针对北欧冬季独特困难的车辆检测问题仍未得到充分解决。本研究采用包含瑞典北部无人机影像的北欧车辆数据集（NVD），评估了前沿车辆检测算法在恶劣天气条件下的表现。我们的方法论包括对基于单阶段、两阶段及Transformer的检测器进行系统性评估，并针对不同检测框架提出系列改进方案，涵盖数据增强、超参数调优、迁移学习以及专为DETR模型设计的创新策略。研究结果不仅揭示了现有检测系统在北欧环境中的局限性，更为提升算法在复杂冬季景观中的鲁棒性和准确性指明了优化方向。相关代码与数据集可通过 https://nvd.ltu-ai.dev 获取。