  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.

Computer Vision and Pattern Recognition