NightKing-V/CarDD

---
annotations_creators: []
language: en
size_categories:
- 1K<n<10K
task_categories:
- object-detection
task_ids: []
pretty_name: car_dd
tags:
- fiftyone
- image
- object-detection
dataset_summary: '

This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 2816 samples.

## Installation

If you haven''t already, install FiftyOne:

```bash

pip install -U fiftyone

```

## Usage

```python

import fiftyone as fo

from fiftyone.utils.huggingface import load_from_hub

# Load the dataset

# Note: other available arguments include ''max_samples'', etc

dataset = load_from_hub("harpreetsahota/CarDD")

# Launch the App

session = fo.launch_app(dataset)

```

'
---

# 🚘 CarDD Dataset


CarDD is a novel, public, large-scale dataset specifically designed for vision-based car damage detection and segmentation.

The dataset contains **4,000 high-resolution car damage images** with over **9,000 well-annotated instances**, making it the largest public dataset of its kind.

The high resolution of the images (average 684,231 pixels) is a key advantage over existing datasets that have a much lower average resolution (50,334 pixels). Higher resolution allows for more detailed annotations and the potential to detect finer damages.

### CarDD Dataset Overview and Features

CarDD features **six common external car damage categories**, chosen based on frequency of occurrence and clear definitions from insurance claim statistics.

1. Dent

2. Scratch

3. Crack

4. Glass shatter

5. Tire flat

6. Lamp broken

### Annotation process
The **annotation process** involved experts from the car insurance industry and trained annotators following specific guidelines based on insurance claim standards.

These guidelines address challenges like

• mixed damages (priority rules)

• damages across components (boundary splitting)

• adjacent same-class damages (boundary merging).

For object detection and instance segmentation, the annotations include **masks and bounding boxes** associated with each of the six damage types.

Each instance has a unique ID, category information, mask contours, and bounding box coordinates, following the COCO dataset format.

For SOD, pixel-level binary ground truth maps are provided.

### Dataset splits
The dataset is split into **training (70.4%), validation (20.25%), and test (9.35%) sets**, maintaining a consistent ratio of instances for each category across the splits.

Near-duplicate images were explicitly removed to prevent data leakage.

### Uses

The dataset provides **comprehensive annotations for multiple computer vision tasks**, including:

* **Classification:** Identifying the type of damage.

* **Object Detection:** Locating the damaged regions with bounding boxes.

* **Instance Segmentation:** Precisely outlining the damaged areas with pixel-level masks.

* **Salient Object Detection (SOD):** Identifying the damaged regions as salient objects through binary maps.

CarDD presents several **challenges** for model development due to the nature of car damage:

* **Fine-grained distinctions** between damage types like dents and scratches.

* **Diversity in object scales and shapes** of the damages.

* A **significant proportion of small objects**, particularly for dent, scratch, and crack categories.

* The fact that damages like **dent, scratch, and crack can be intertwined and visually similar**.

#### Availability

The CarDD dataset is **publicly available** at https://cardd-ustc.github.io.

However, access requires agreeing to the license terms of Flickr and Shutterstock, as the dataset does not own the copyright of the images.

The dataset is intended for non-commercial research and educational purposes.

Measures were taken to protect user privacy by mosaicking or deleting faces and license plates.

# Citation

```bibtex
@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}}
```