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Autore principale: Chen, Hanxiao
Natura: Preprint
Pubblicazione: 2024
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Accesso online:https://arxiv.org/abs/2403.06674
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author Chen, Hanxiao
author_facet Chen, Hanxiao
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2403_06674
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Car Damage Detection and Patch-to-Patch Self-supervised Image Alignment
Chen, Hanxiao
Computer Vision and Pattern Recognition
Artificial Intelligence
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.
title Car Damage Detection and Patch-to-Patch Self-supervised Image Alignment
topic Computer Vision and Pattern Recognition
Artificial Intelligence
url https://arxiv.org/abs/2403.06674