DAWN: Vehicle Detection in Adverse Weather Nature

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，多款自动驾驶车辆已搭载多项自动化功能，包括车道保持辅助、拥堵跟驰辅助、泊车辅助以及碰撞规避功能。此类自动驾驶车辆与智能视觉交通监控系统，主要依赖摄像头与传感器融合系统。诸如大雾、降雨、降雪与沙尘暴等恶劣天气，会对摄像头的成像功能造成严重限制，进而显著影响用于场景理解的计算机视觉算法的性能——此类算法需完成交通场景中的车辆检测、跟踪与识别任务。例如，路面雨滴与冰层产生的反射，可能引发严重的检测误差，进而削弱智能视觉交通系统的整体性能。此外，当前场景理解与车辆检测算法的评估，大多基于仅包含少量真实图像与特定类型合成图像的数据集。因此，目前尚不清楚这些算法在"野外"场景采集的模糊图像上的表现，也无法明确该领域内此类算法的性能评估标准该如何统一。为此，我们构建了一个全新的数据集（基准测试集），其包含在各类恶劣天气条件下采集的真实交通场景图像，命名为DAWN。该数据集覆盖了多样化的交通场景（城市道路、公路与高速公路），并包含丰富的交通流类型。DAWN数据集共收录1000张真实交通场景图像，按恶劣天气类型分为四类：大雾、降雪、降雨与沙尘暴。该数据集已针对自动驾驶与视频监控场景，完成了目标边界框标注。本数据集可用于分析恶劣天气对车辆检测系统性能造成的影响。

搜集汇总

背景与挑战

背景概述

DAWN数据集是一个专注于恶劣天气条件下车辆检测的基准数据集，包含1000张真实交通环境图像，覆盖雾、雪、雨和沙尘暴四种天气类型，并提供了车辆、行人等多类对象的边界框标注。该数据集旨在评估自动驾驶和交通监控系统中计算机视觉算法在复杂天气下的性能，弥补了现有数据集中合成图像居多的不足，具有实际应用价值。