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

• URL: http://arxiv.org/abs/2401.09245v2
• Date: Fri, 17 May 2024 08:05:18 GMT
• Title: Uncertainty estimates for semantic segmentation: providing enhanced reliability for automated motor claims handling
• Authors: Jan Küchler, Daniel Kröll, Sebastian Schoenen, Andreas Witte,
• Abstract summary: We use a meta-classification model to empirically assess the precision of segments predicted by a model trained for the semantic segmentation of car body parts. 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%.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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%.

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