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
this https URL

本研究使用北欧地区的无人机图像数据集（Nordic Vehicle Dataset）评估了最先进的车辆检测算法在恶劣天气条件下的性能，并针对其中的困难问题提出了一系列针对不同检测框架的增强方法，包括数据增强、超参数调整、迁移学习以及针对 DETR 模型的新策略。研究结果不仅凸显了当前车辆检测系统在北欧环境中存在的局限性，而且为改善这些算法在冬季景观的复杂性中的鲁棒性和准确性提供了有希望的方向。

北欧地区的车辆检测性能

Vehicle Detection Performance in Nordic Region

Winter conditions pose several challenges for automated driving applications.
A key challenge during winter is accurate assessment of road surface condition,
as its impact on friction is a critical parameter for safely and reliably
controlling a vehicle. This paper proposes a deep learning regression model,
SIWNet, capable of estimating road surface friction properties from camera
images. SIWNet extends state of the art by including an uncertainty estimation
mechanism in the architecture. This is achieved by including an additional head
in the network, which estimates a prediction interval. The prediction interval
head is trained with a maximum likelihood loss function. The model was trained
and tested with the SeeingThroughFog dataset, which features corresponding road
friction sensor readings and images from an instrumented vehicle. Acquired
results highlight the functionality of the prediction interval estimation of
SIWNet, while the network also achieved similar point estimate accuracy as the
previous state of the art. Furthermore, the SIWNet architecture is several
times more lightweight than the previously applied state-of-the-art model,
resulting in more practical and efficient deployment.

该研究提出了一种名为 SIWNet 的深度学习回归模型，能够从摄像头图像中准确估计道路表面摩擦特性，该模型在网络中引入了一个额外的头部来估计预测区间，通过最大似然损失函数进行训练。研究结果表明，SIWNet 的预测区间估计功能良好，并且与之前的最先进模型在点估计准确度上相当，而且 SIWNet 的架构比之前的模型轻量化了几倍，更加适合实际部署和高效使用。