DAWN: Vehicle Detection Dataset in Adverse Weather Nature
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Recently, self-driving vehicles have been introduced with several automated features including lane-keep assistance, queuing assistance in traffic-jam, parking assistance and crash avoidance. These self-driving vehicles and intelligent visual traffic surveillance systems mainly depend on cameras and sensors fusion systems. Adverse weather conditions such as heavy fog, rain, snow, and sandstorms are considered dangerous restrictions of the functionality of cameras impacting seriously the performance of adopted computer vision algorithms for scene understanding (i.e., vehicle detection, tracking, and recognition in traffic scenes). For example, reflection coming from rain flow and ice over roads could cause massive detection errors which will affect the performance of intelligent visual traffic systems. Additionally, scene understanding and vehicle detection algorithms are mostly evaluated using datasets contain certain types of synthetic images plus a few real-world images. Thus, it is uncertain how these algorithms would perform on unclear images acquired “in the wild” and how the progress of these algorithms is standardized in the field. To this end, we present a new dataset (benchmark) consisting of real-world images collected under various adverse weather conditions called DAWN. This dataset emphasizes a diverse traffic environment (urban, highway and freeway) as well as a rich variety of traffic flow. The DAWN dataset comprises a collection of 1000 images from real-traffic environments, which are divided into four sets of weather conditions: fog, snow, rain and sandstorms. The dataset is annotated with object bounding boxes for autonomous driving and video surveillance scenarios. This data helps interpreting effects caused by the adverse weather conditions on the performance of vehicle detection systems.
近年来，自动驾驶车辆已搭载多项自动化功能，涵盖车道保持辅助（lane-keep assistance）、拥堵路段排队辅助、泊车辅助与碰撞避免功能。此类自动驾驶车辆与智能视觉交通监控系统，主要依托相机与传感器融合系统（cameras and sensors fusion systems）。大雾、降雨、降雪、沙尘暴等恶劣天气条件，会对相机功能构成显著限制，严重影响用于场景理解的计算机视觉算法（computer vision algorithms）的性能——此类算法需在交通场景中完成车辆检测、跟踪与识别任务。例如，路面雨滴与冰层产生的反射易引发大规模检测误差，进而影响智能视觉交通系统的运行性能。此外，当前主流的场景理解与车辆检测算法，大多采用仅包含特定类型合成图像与少量真实图像的数据集开展评估，因此难以判断这些算法在“野外”采集的模糊图像上的实际表现，也无法实现该领域内算法进展的标准化评测。为此，我们推出了一款全新的数据集（基准测试集，benchmark），命名为DAWN，其收录了各类恶劣天气条件下采集的真实世界图像。该数据集覆盖了多样化的交通环境，包括城市道路、普通公路与高速公路，同时包含丰富的交通流场景。DAWN数据集共包含1000张来自真实交通环境的图像，按雾天、雪天、雨天与沙尘暴四种天气条件划分为四组。本数据集针对自动驾驶与视频监控场景中的目标标注了边界框（bounding boxes），可用于分析恶劣天气条件对车辆检测系统性能造成的影响。
