Car Damage Detection and Patch-to-Patch Self-supervised Image Alignment
<|reference_start|>Car Damage Detection and Patch-to-Patch Self-supervised Image Alignment: 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.<|reference_end|>
@article{chen2024car,
    title={Car Damage Detection and Patch-to-Patch Self-supervised Image Alignment},
    author={Hanxiao Chen},
    journal={arXiv preprint arXiv:2403.06674},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.06674},
    primaryClass={cs.CV cs.AI}
}