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 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可作为保险行业自动化机动车理赔处理流程的有力工具。一个关键方面是模型在面对不利条件（如索赔人拍摄的低质量损坏照片）时输出的可靠性。我们探索使用元分类模型来评估针对汽车车身部件语义分割训练的模型所预测分割的精度。比较了与分割质量相关的不同特征集，在区分高、低质量分割时达到了0.915的AUROC分数。通过移除低质量分割，分割输出的平均mIoU提升了16个百分点，错误预测的分割数量减少了77%。