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

arXiv: https://arxiv.org/abs/2403.06674 · HTML: https://mlsanity.com/paper/2403.06674/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 6 references that are in the map.
- First posted on arXiv 11 Mar 2024.

## Builds on (most cited first, 5 of 5)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [SkateboardAI: The Coolest Video Action Recognition for Skateboarding](https://mlsanity.com/paper/2311.11467/) (2023)
- [Color-S4L: Self-supervised Semi-supervised Learning with Image Colorization](https://mlsanity.com/paper/2401.03753/) (2024)

## Built on by (most cited first, 1 of 1)

- [CrashSplat: 2D to 3D Vehicle Damage Segmentation in Gaussian Splatting](https://mlsanity.com/paper/2509.23947/) (2025)

## Abstract (arXiv)

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.