  Segmenting each moving object instance in a scene is essential for many
applications. But like many other computer vision tasks, this task performs
well in optimal weather, but then adverse weather tends to fail. To be robust
in weather conditions, the usual way is to train network in data of given
weather pattern or to fuse multiple sensors. We focus on a new possibility,
that is, to improve its resilience to weather interference through the
network's structural design. First, we propose a novel FPN structure called
RiWFPN with a progressive top-down interaction and attention refinement module.
RiWFPN can directly replace other FPN structures to improve the robustness of
the network in non-optimal weather conditions. Then we extend SOLOV2 to capture
temporal information in video to learn motion information, and propose a moving
object instance segmentation network with RiWFPN called RiWNet. Finally, in
order to verify the effect of moving instance segmentation in different weather
disturbances, we propose a VKTTI-moving dataset which is a moving instance
segmentation dataset based on the VKTTI dataset, taking into account different
weather scenes such as rain, fog, sunset, morning as well as overcast. The
experiment proves how RiWFPN improves the network's resilience to adverse
weather effects compared to other FPN structures. We compare RiWNet to several
other state-of-the-art methods in some challenging datasets, and RiWNet shows
better performance especially under adverse weather conditions.
