Uncertainty estimates for semantic segmentation: providing enhanced
  reliability for automated motor claims handling
Jan Küchler Daniel Kröll Sebastian Schoenen Andreas Witte
Deep neural network models for image segmentation can be a powerful tool for
the automation of motor claims handling processes in the insurance industry. A
crucial aspect is the reliability of the model outputs when facing adverse
conditions, such as low quality photos taken by claimants to document damages.
We explore the use of a meta-classification model to assess the precision of
segments predicted by a model trained for the semantic segmentation of car body
parts. Different sets of features correlated with the quality of a segment are
compared, and an AUROC score of 0.915 is achieved for distinguishing between
high- and low-quality segments. By removing low-quality segments, the average
mIoU of the segmentation output is improved by 16 percentage points and the
number of wrongly predicted segments is reduced by 77%.