Most computer vision applications aim to identify pixels in a scene and use them for diverse purposes. One intriguing application is car damage detection for insurance carriers which tends to detect all car damages by comparing both pre-trip and post-trip images, even requiring two components: (i) car damage detection; (ii) image alignment. Firstly, we implemented a Mask R-CNN model to detect car damages on custom images. Whereas for the image alignment section, we especially propose a novel self-supervised Patch-to-Patch SimCLR inspired alignment approach to find perspective transformations between custom pre/post car rental images except for traditional computer vision methods.

翻译：大多数计算机视觉应用旨在识别场景中的像素并将其用于多种目的。其中一个有趣的应用是保险理赔中的车辆损伤检测，它倾向于通过比较出行前和出行后的图像来检测所有车辆损伤，甚至需要两个组成部分：(i) 车辆损伤检测；(ii) 图像对齐。首先，我们实现了Mask R-CNN模型，用于在自定义图像上检测车辆损伤。而在图像对齐部分，我们特别提出了一种新颖的自监督补丁到补丁SimCLR启发式对齐方法，用于找到自定义车辆租赁前/后图像之间的透视变换，该方法比传统计算机视觉方法更具优势。