# Uncertainty estimates for semantic segmentation: providing enhanced reliability for automated motor claims handling

Source: [Uncertainty estimates for semantic segmentation: providing enhanced reliability for automated motor claims handling](<https://www.sota2.com/research/paper/68fa73311e209131cea5b06f>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T02:44:18.752Z

Authors: Jan K\\"uchler, Daniel Kr\\"oll, Sebastian Schoenen, Andreas Witte (1) \_\_INSTITUTION\_4\_\_ ControlExpert GmbH, Langenfeld, Germany)

Published: 2024-01-17T14:47:26.000Z

## Abstract

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 empirically 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%.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2401.09245>)

[PDF](<https://arxiv.org/pdf/2401.09245.pdf>)