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

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

11 Mar 2024arXiv:2403.06674archive 2025-07-28

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.

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