mlsanityData to 7 Oct 2026

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

arXiv 2401.09245 ↗First posted 17 Jan 2024cs.CVcited by 2

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Abstract (arXiv)

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%.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 17 of its 31 references that are in the map. First posted on arXiv 17 Jan 2024.

Builds on

17 of 31 references.

References · 17

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5 more
  1. 2020Estimating Uncertainty in Neural Networks for Cardiac MRI Segmentation: A Benchmark Studycited by 19
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