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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2401.09245 |
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| _version_ | 1866929346565898240 |
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| author | Küchler, Jan Kröll, Daniel Schoenen, Sebastian Witte, Andreas |
| author_facet | Küchler, Jan Kröll, Daniel Schoenen, Sebastian Witte, Andreas |
| contents | 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%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_09245 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Uncertainty estimates for semantic segmentation: providing enhanced reliability for automated motor claims handling Küchler, Jan Kröll, Daniel Schoenen, Sebastian Witte, Andreas Computer Vision and Pattern Recognition 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%. |
| title | Uncertainty estimates for semantic segmentation: providing enhanced reliability for automated motor claims handling |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2401.09245 |