Car Damage Detection and Patch-to-Patch Self-supervised Image Alignment

Most computer vision applications aim to identify pixels in a scene and utilize 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. But for the image alignment section, we especially propose a novel Patch-to-Patch SimCLR inspired image alignment approach to find perspective transformations between custom pre/post car rental images except for traditional methods.