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Recently, self-driving vehicles have been introduced with several automatedfeatures including lane-keep assistance, queuing assistance in traffic-jam,parking assistance and crash avoidance. These self-driving vehicles andintelligent visual traffic surveillance systems mainly depend on cameras andsensors fusion systems. Adverse weather conditions such as heavy fog, rain,snow, and sandstorms are considered dangerous restrictions of the functionalityof cameras impacting seriously the performance of adopted computer visionalgorithms for scene understanding (i.e., vehicle detection, tracking, andrecognition in traffic scenes). For example, reflection coming from rain flowand ice over roads could cause massive detection errors which will affect theperformance of intelligent visual traffic systems. Additionally, sceneunderstanding and vehicle detection algorithms are mostly evaluated usingdatasets contain certain types of synthetic images plus a few real-worldimages. Thus, it is uncertain how these algorithms would perform on unclearimages acquired in the wild and how the progress of these algorithms isstandardized in the field. To this end, we present a new dataset (benchmark)consisting of real-world images collected under various adverse weatherconditions called DAWN. This dataset emphasizes a diverse traffic environment(urban, highway and freeway) as well as a rich variety of traffic flow. TheDAWN dataset comprises a collection of 1000 images from real-trafficenvironments, which are divided into four sets of weather conditions: fog,snow, rain and sandstorms. The dataset is annotated with object bounding boxesfor autonomous driving and video surveillance scenarios. This data helpsinterpreting effects caused by the adverse weather conditions on theperformance of vehicle detection systems.

DAWN: Vehicle Detection in Adverse Weather Nature Dataset