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%.

翻译：深度神经网络模型在图像分割中的应用可为保险行业自动化机动车辆理赔处理流程提供强大工具。关键挑战在于模型输出在面临不利条件（如索赔人拍摄的低质量损坏照片）时的可靠性。本文探索使用元分类模型，实证评估针对汽车车身部件语义分割训练的模型所预测分割区域的精度。我们比较了与分割质量相关的不同特征集，并在区分高质量与低质量分割区域时实现了0.915的AUROC分数。通过移除低质量分割区域，分割输出的平均mIoU提升了16个百分点，错误预测的分割区域数量减少了77%。