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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
"http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
<html xmlns="http://www.w3.org/1999/xhtml">
<head>
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
<title>CarDD: A New Dataset for Vision-based Car Damage Detection</title>
<!-- Meta tags for search engines to crawl -->
<meta name="robots" content="index,follow">
<meta name="description"
content="Automatic car damage detection has attracted significant attention in the car insurance business. However, due to the lack of high-quality and publicly available datasets, we can hardly learn a feasible model for car damage detection. To this end, we contribute with Car Damage Detection (CarDD), the first public large-scale dataset designed for vision-based car damage detection and segmentation. Our CarDD contains 4,000 high-resolution car damage images with over 9,000 wellannotated instances of six damage categories (examples are shown in Figure 1). We detail the image collection, selection, and annotation processes, and present a statistical dataset analysis. Furthermore, we conduct extensive experiments on CarDD with state-of-the-art deep methods for different tasks and provide comprehensive analyses to highlight the specialty of car damage detection. CarDD will be publicly available soon. "
>
<meta name="keywords" content="Car damage, New dataset, Object detection, Instance segmentation, Salient object detection (SOD)">
<link rel="author" href="Xinkuang Wang, Wenjing Li, Zhongcheng Wu">
<!-- Fonts and stuff -->
<link href="./css" rel="stylesheet" type="text/css">
<link rel="stylesheet" type="text/css" href="./project.css" media="screen">
<link rel="stylesheet" type="text/css" media="screen" href="./iconize.css">
<script async="" src="./prettify.js"></script>
</head>
<body>
<div id="content">
<div id="content-inner">
<div class="section head">
<h1>CarDD: A New Dataset for Vision-based Car Damage Detection</h1>
<div class="authors">
<a >Xinkuang Wang</a><sup>1,2</sup>
<a >Wenjing Li</a><sup>1,2</sup>
<a >Zhongcheng Wu</a><sup>1,2</sup>
</div>
<div class="affiliations">
1. Chinese Academy of Sciences, China <br>
2. University of Science and Technology of China, China <br>
</div>
<div class="affiliations">
</div>
</div>
<center><img src="/img/Annotation_visualization.png" border="0" width="95%"><br>
Samples of annotated images in CarDD dataset.
</center>
<div class="section abstract">
<h2>Abstract</h2>
<br>
<p>Automatic car damage detection has attracted significant attention in the car insurance business. However, due to the lack of high-quality and publicly available datasets, we can hardly learn a feasible model for car damage detection. To this end, we contribute with Car Damage Detection (CarDD), the first public large-scale dataset designed for vision-based car damage detection and segmentation. Our CarDD contains 4,000 high-resolution car damage images with over 9,000 wellannotated instances of six damage categories (examples are shown in Figure 1). We detail the image collection, selection, and annotation processes, and present a statistical dataset analysis. Furthermore, we conduct extensive experiments on CarDD with state-of-the-art deep methods for different tasks and provide comprehensive analyses to highlight the specialty of car damage detection.
</p>
</div>
<div class="section method">
<h2>CarDD Overview</h2>
<br>
<center><img src="/img/Statistics.png" border="0" width="95%"><br>
</center>
</div>
<!--=================Materials==========================-->
<div class="section materials" , id="materials">
<h2>Materials</h2>
<table width="80%" align="center" border=none cellspacing="0" cellpadding="30">
<tr>
<td width="30%">
<center>
<a href="/docs/CarDD.pdf" target="_blank" class="imageLink"><img
src="/img/CarDD_thumb.png" , width="75%"></a><br><br>
<a href="/docs/CarDD.pdf" target="_blank">Paper</a>
</center>
</td>
<td width="30%">
<center>
<a href="https://drive.google.com/file/d/1bbyqVCKZX5Ur5Zg-uKj0jD0maWAVeOLx/view?usp=sharing" target="_blank" class="imageLink"><img
src="/img/googledrive.png", width="60%"></a><br><br>
<a href="https://drive.google.com/file/d/1bbyqVCKZX5Ur5Zg-uKj0jD0maWAVeOLx/view?usp=sharing" target="_blank">Dataset</a>
</center>
</td>
<td width="30%" valign="middle">
<center>
<a href="https://github.com/CarDD-USTC/CarDD-USTC.github.io" target="_blank"
class="imageLink"><img
src="/img/icon_github.png" , width="60%"></a><br><br>
<a href="https://github.com/CarDD-USTC/CarDD-USTC.github.io" target="_blank">Codes</a>
</center>
</td>
</tr>
</table>
NOTE: For downloading the data, please first fill in the licensing form and send us to get the link. <a href="/docs/CarDD_license.pdf" target="_blank">[Licensing Form]</a>
</div>
<br>
<div class="section citation">
<h2>Citation</h2>
<div class="section bibtex">
<pre>@article{CarDD,
author={Wang, Xinkuang and Li, Wenjing and Wu, Zhongcheng},
journal={IEEE Transactions on Intelligent Transportation Systems},
title={CarDD: A New Dataset for Vision-Based Car Damage Detection},
year={2023},
volume={24},
number={7},
pages={7202-7214},
doi={10.1109/TITS.2023.3258480}
}</pre>
</div>
</div>
</body>
</html>