Clear Weather 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.

本研究所提出的数据集涵盖在恶劣天气条件下的目标检测，包含12000个现实驾驶场景样本和1500个在雾室控制的天气条件下的样本。数据集涵盖了诸如雾、雪和雨等多种天气状况，并经由北欧超过10000公里的驾驶采集而成。道路沿线城市的行驶路线如图所示。总计10万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如下：- 我们提供了一个广泛算法的测试平台，包括信号增强、领域自适应、目标检测或多模态传感器融合，重点关注传感器间稳健冗余的学习，尤其是在不同天气条件下传感器非对称性失效的情况下。- 数据集的创建初衷是为了展示学习传感器间稳健冗余的方法，以便在由不良天气效应引起的非对称传感器失效情况下实现原始数据传感器融合。- 在本案例中，我们偏离了提案级融合，采用由测量熵驱动的自适应融合，即使在未知的不良天气效应下也能实现检测。该方法优于其他参考融合方法，甚至超越了单图像方法。- 欲了解更多信息，请参阅我们的论文。