  In the semantic segmentation of street scenes the reliability of the
prediction and therefore uncertainty measures are of highest interest. We
present a method that generates for each input image a hierarchy of nested
crops around the image center and presents these, all re-scaled to the same
size, to a neural network for semantic segmentation. The resulting softmax
outputs are then post processed such that we can investigate mean and variance
over all image crops as well as mean and variance of uncertainty heat maps
obtained from pixel-wise uncertainty measures, like the entropy, applied to
each crop's softmax output. In our tests, we use the publicly available
DeepLabv3+ MobilenetV2 network (trained on the Cityscapes dataset) and
demonstrate that the incorporation of crops improves the quality of the
prediction and that we obtain more reliable uncertainty measures. These are
then aggregated over predicted segments for either classifying between IoU=0
and IoU>0 (meta classification) or predicting the IoU via linear regression
(meta regression). The latter yields reliable performance estimates for
segmentation networks, in particular useful in the absence of ground truth. For
the task of meta classification we obtain a classification accuracy of
$81.93\%$ and an AUROC of $89.89\%$. For meta regression we obtain an $R^2$
value of $84.77\%$. These results yield significant improvements compared to
other approaches.
