Summary of Car Damage Detection and Patch-to-patch Self-supervised Image Alignment, by Hanxiao Chen
Car Damage Detection and Patch-to-Patch Self-supervised Image Alignment
by Hanxiao Chen
First submitted to arxiv on: 11 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Most computer vision applications aim to identify pixels in a scene and use them for diverse purposes. This paper proposes a novel approach to car damage detection for insurance carriers, which involves detecting damages using Mask R-CNN and aligning pre-trip and post-trip images using a self-supervised Patch-to-Patch SimCLR inspired alignment method. The proposed approach is designed to overcome the limitations of traditional computer vision methods by leveraging self-supervised learning. The model detects car damages on custom images and aligns perspective transformations between pre- and post-car rental images, enabling accurate damage detection and insurance claims processing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to detect car damages for insurance companies. It uses a special kind of AI called Mask R-CNN to find damaged areas in pictures taken before and after a car is rented. The approach also aligns the two sets of images so that they match up correctly. This helps insurance companies accurately determine if a car has been damaged during rental. |
Keywords
» Artificial intelligence » Alignment » Cnn » Mask » Self supervised
