  Computer vision tasks such as semantic segmentation perform very well in good
weather conditions, but if the weather turns bad, they have problems to achieve
this performance in these conditions. One possibility to obtain more robust and
reliable results in adverse weather conditions is to use video-segmentation
approaches instead of commonly used single-image segmentation methods.
Video-segmentation approaches capture temporal information of the previous
video-frames in addition to current image information, and hence, they are more
robust against disturbances, especially if they occur in only a few frames of
the video-sequence. However, video-segmentation approaches, which are often
based on recurrent neural networks, cannot be applied in real-time applications
anymore, since their recurrent structures in the network are computational
expensive. For instance, the inference time of the LSTM-ICNet, in which
recurrent units are placed at proper positions in the single-segmentation
approach ICNet, increases up to 61 percent compared to the basic ICNet. Hence,
in this work, the LSTM-ICNet is sped up by modifying the recurrent units of the
network so that it becomes real-time capable again. Experiments on different
datasets and various weather conditions show that the inference time can be
decreased by about 23 percent by these modifications, while they achieve
similar performance than the LSTM-ICNet and outperform the single-segmentation
approach enormously in adverse weather conditions.
