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

Autor:	Chen, Hanxiao
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	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. Comment: The paper has been accepted and given a poster presentation at NeurIPS 2021 WiML Workshop (https://nips.cc/virtual/2021/affinity-workshop/22882)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2403.06674 Zobrazit plný text záznamu View this record from Arxiv