Car Damage Detection and Patch-to-Patch Self-supervised Image Alignment
Hanxiao Chen
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.