Car Damage Detection and Patch-to-Patch Self-supervised Image Alignment
This addresses car damage detection for insurance carriers, but it appears incremental as it combines existing methods with a new alignment approach.
The paper tackled car damage detection for insurance by implementing a Mask R-CNN model to detect damages and proposing a novel self-supervised Patch-to-Patch SimCLR approach for aligning pre- and post-trip images, achieving unspecified results without concrete numbers.
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.