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--- |
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annotations_creators: [] |
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language: en |
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size_categories: |
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- 1K<n<10K |
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task_categories: |
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- object-detection |
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task_ids: [] |
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pretty_name: car_dd |
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tags: |
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- fiftyone |
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- image |
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- object-detection |
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dataset_summary: ' |
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This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 2816 samples. |
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## Installation |
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If you haven''t already, install FiftyOne: |
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```bash |
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pip install -U fiftyone |
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``` |
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## Usage |
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```python |
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import fiftyone as fo |
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from fiftyone.utils.huggingface import load_from_hub |
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# Load the dataset |
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# Note: other available arguments include ''max_samples'', etc |
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dataset = load_from_hub("harpreetsahota/CarDD") |
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# Launch the App |
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session = fo.launch_app(dataset) |
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``` |
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' |
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--- |
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# 🚘 CarDD Dataset |
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CarDD is a novel, public, large-scale dataset specifically designed for vision-based car damage detection and segmentation. |
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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. |
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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. |
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### CarDD Dataset Overview and Features |
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CarDD features **six common external car damage categories**, chosen based on frequency of occurrence and clear definitions from insurance claim statistics. |
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1. Dent |
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2. Scratch |
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3. Crack |
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4. Glass shatter |
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5. Tire flat |
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6. Lamp broken |
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### Annotation process |
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The **annotation process** involved experts from the car insurance industry and trained annotators following specific guidelines based on insurance claim standards. |
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These guidelines address challenges like |
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• mixed damages (priority rules) |
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• damages across components (boundary splitting) |
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• adjacent same-class damages (boundary merging). |
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For object detection and instance segmentation, the annotations include **masks and bounding boxes** associated with each of the six damage types. |
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Each instance has a unique ID, category information, mask contours, and bounding box coordinates, following the COCO dataset format. |
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For SOD, pixel-level binary ground truth maps are provided. |
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### Dataset splits |
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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. |
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Near-duplicate images were explicitly removed to prevent data leakage. |
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### Uses |
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The dataset provides **comprehensive annotations for multiple computer vision tasks**, including: |
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* **Classification:** Identifying the type of damage. |
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* **Object Detection:** Locating the damaged regions with bounding boxes. |
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* **Instance Segmentation:** Precisely outlining the damaged areas with pixel-level masks. |
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* **Salient Object Detection (SOD):** Identifying the damaged regions as salient objects through binary maps. |
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CarDD presents several **challenges** for model development due to the nature of car damage: |
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* **Fine-grained distinctions** between damage types like dents and scratches. |
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* **Diversity in object scales and shapes** of the damages. |
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* A **significant proportion of small objects**, particularly for dent, scratch, and crack categories. |
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* The fact that damages like **dent, scratch, and crack can be intertwined and visually similar**. |
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#### Availability |
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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. |
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# Citation |
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```bibtex |
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@ARTICLE{CarDD, author={Wang, Xinkuang and Li, Wenjing and Wu, Zhongcheng}, |
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journal={IEEE Transactions on Intelligent Transportation Systems}, |
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title={CarDD: A New Dataset for Vision-Based Car Damage Detection}, |
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year={2023}, |
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volume={24}, |
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number={7}, |
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pages={7202-7214}, |
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doi={10.1109/TITS.2023.3258480}} |
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``` |
