Duplicate from harpreetsahota/CarDD

Co-authored-by: Harpreet Sahota <harpreetsahota@users.noreply.huggingface.co>

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CarDD_license.pdf

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README.md

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+---
+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
+
+![image](cardd-overview-lq.gif)
+
+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}}
+```