Dense Fog Dataset

We introduce an object detection dataset in challenging adverse weather conditions covering 12000 samples in real-world driving scenes and 1500 samples in controlled weather conditions within a fog chamber. The dataset includes different weather conditions like fog, snow, and rain and was acquired by over 10,000 km of driving in northern Europe. The driven route with cities along the road is shown on the right. In total, 100k Objekts were labeled with accurate 2D and 3D bounding boxes. The main contributions of this dataset are:
- We provide a proving ground for a broad range of algorithms covering signal enhancement, domain adaptation, object detection, or multi-modal sensor fusion, focusing on the learning of robust redundancies between sensors, especially if they fail asymmetrically in different weather conditions.
- The dataset was created with the initial intention to showcase methods, which learn of robust redundancies between the sensor and enable a raw data sensor fusion in case of asymmetric sensor failure induced through adverse weather effects.
- In our case we departed from proposal level fusion and applied an adaptive fusion driven by measurement entropy enabling the detection also in case of unknown adverse weather effects. This method outperforms other reference fusion methods, which even drop in below single image methods.
- Please check out our paper for more information.

本数据集涵盖在恶劣天气条件下进行的目标检测，包含真实驾驶场景中的12000个样本以及在雾室控制天气条件下的1500个样本。该数据集囊括了诸如雾、雪和雨等不同的天气状况，通过在欧洲北部超过10000公里的驾驶行程中所采集。道路沿线城市的驾驶路线如图所示。总计100k个物体被标注了精确的2D和3D边界框。本数据集的主要贡献包括：
- 我们提供了一个广泛的算法验证平台，涵盖信号增强、域适应、目标检测或多模态传感器融合等领域，重点在于学习传感器之间稳健的冗余性，特别是在不同天气条件下传感器非对称性故障的情况下。
- 该数据集的创建初衷在于展示能够学习传感器之间稳健冗余性的方法，并在传感器因恶劣天气影响导致的非对称性故障时实现原始数据传感器融合。
- 在我们的案例中，我们偏离了提案级融合，并采用由测量熵驱动的自适应融合，即使在未知恶劣天气效应的情况下也能实现检测。该方法优于其他参考融合方法，甚至超越了单图像方法。
- 请参阅我们的论文以获取更多信息。