Evaluation of car damages from an accident is one of the most important processes in the car insurance business. Currently, it still needs a manual examination of every basic part. It is expected that a smart device will be able to do this evaluation more efficiently in the future. In this study, we evaluated and compared five deep learning algorithms for semantic segmentation of car parts. The baseline reference algorithm was Mask R-CNN, and the other algorithms were HTC, CBNet, PANet, and GCNet. Runs of instance segmentation were conducted with those five algorithms. HTC with ResNet-50 was the best algorithm for instance segmentation on various kinds of cars such as sedans, trucks, and SUVs. It achieved a mean average precision at 55.2 on our original data set, that assigned different labels to the left and right sides and 59.1 when a single label was assigned to both sides. In addition, the models from every algorithm were tested for robustness, by running them on images of parts, in a real environment with various weather conditions, including snow, frost, fog and various lighting conditions. GCNet was the most robust; it achieved a mean performance under corruption, mPC = 35.2, and a relative degradation of performance on corrupted data, compared to clean data (rPC), of 64.4%, when left and right sides were assigned different labels, and mPC = 38.1 and rPC = \(69.6\%\) when left- and right-side parts were considered the same part. The findings from this study may directly benefit developers of automated car damage evaluation system in their quest for the best design.

评估事故造成的汽车损坏是汽车保险业务中最重要的过程之一。当前，它仍然需要对每个基本部分进行手动检查。预计将来智能设备将能够更有效地进行此评估。在这项研究中，我们评估并比较了五种深度学习算法用于汽车零件的语义分割。基线参考算法是Mask R-CNN，其他算法是HTC，CBNet，PANet和GCNet。使用这五种算法进行了实例分割。带有ResNet-50的HTC是在诸如轿车，卡车和SUV之类的各种汽车上进行实例细分的最佳算法。在原始数据集上，它的平均平均精度为55.2，为左侧和右侧以及59分配了不同的标签。当将单个标签分配给双面时为1。此外，在具有各种天气条件（包括雪，霜，雾和各种光照条件）的真实环境中，通过在零件图像上运行它们，测试了每种算法的模型的鲁棒性。GCNet最强大。在左侧和右侧分别分配了不同的标签，并且mPC = 38.1和rPC时，它在损坏情况下的平均性能为mPC = 35.2，与原始数据（rPC）相比，损坏数据的性能相对下降了64.4％。 =如果将左侧和右侧部分视为同一部分，则为\（69.6 \％\）。这项研究的结果可能会直接使自动汽车损坏评估系统的开发人员受益，以寻求最佳设计。