README.md

---
license: mit
task_categories:
- object-detection
size_categories:
- 10K<n<100K
---

# Dataset Card for Drone Detection Dataset

<!-- Provide a quick summary of the dataset. -->

This dataset card describes the processed version of the Drone Detection Dataset, originally curated by Maciej Pawełczyk and Marek Wojtyra, adapted to a COCO-style format for efficient usage in modern deep learning pipelines

## Dataset Details

### Dataset Description

The Drone Detection Dataset is a real-world object detection dataset for UAV detection tasks. It includes RGB images annotated with bounding boxes in the COCO format. This dataset is ideal for training and evaluating object detection models like Faster R-CNN, YOLO, and DETR.


- **Curated by:** Maciej Pawełczyk, Marek Wojtyra
- **Shared by:** [pathikg](https://huggingface.co/pathikg)
- **Language(s) (NLP):** Not applicable
- **License:** MIT License

### Dataset Sources

<!-- Provide the basic links for the dataset. -->

- **Repository:** https://github.com/Maciullo/DroneDetectionDataset
- **Paper:** https://ieeexplore.ieee.org/document/9205392

## Uses

<!-- Address questions around how the dataset is intended to be used. -->

### Direct Use

<!-- This section describes suitable use cases for the dataset. -->

This dataset is suitable for:

- Training object detection models.
- Research on UAV detection and monitoring in various environments and lighting conditions.


### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->

Out-of-Scope Use
- The dataset is not ideal for real-time UAV tracking.
- It may not generalize well to unseen drone types or environments without further fine-tuning.

## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

The dataset is structured as a Hugging Face DatasetDict with train and test splits.
Each split contains features:

```python
DatasetDict({
    train: Dataset({
        features: ['width', 'height', 'objects', 'image', 'image_id'],
        num_rows: 51446
    }),
    test: Dataset({
        features: ['width', 'height', 'objects', 'image', 'image_id'],
        num_rows: 2625
    })
})
```

### Example Datapoint

```python
{
 'width': 640,
 'height': 480,
 'objects': {
    'bbox': [[281.0, 210.0, 25.0, 19.0]],  # COCO format: [x, y, width, height]
    'category': [0],  # Category index for the drone
    'area': [475.0],  # Area of the bounding box
    'id': [0]         # Object ID
 },
 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480>,
 'image_id': 2
}
```

### Features
- width and height: Dimensions of the image.
- objects:
  - bbox: Bounding boxes in COCO format [x, y, width, height].
  - category: Class labels (0 for drones).
  - area: Area of each bounding box.
  - id: Unique ID for each object.
- image: The image as a PIL object.
- image_id: Unique identifier for the image.

## Dataset Creation

### Curation Rationale

<!-- Motivation for the creation of this dataset. -->

The dataset was designed to facilitate UAV detection, addressing challenges like detecting small objects across varying scales and environments. It focuses on static object detection rather than tracking.

### Source Data

<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->

#### Data Collection and Processing

<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->

- Source: Frames extracted from publicly available YouTube videos.
- Processing: Bounding boxes were annotated manually in COCO format for initial samples and later supplemented with automated annotations using trained models.

#### Who are the source data producers?

<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->

The source videos were created by various YouTube users. The dataset annotations were curated as part of the authors' research.

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

- Bias: The dataset includes drones in specific environments and lighting conditions, which may limit its generalizability.
- Limitations:
  - The dataset does not cover all drone types or edge cases.
  - It may not be suitable for real-time applications without modifications.

## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

```
@article{pawelczyk2020drone,
  title={Real World Object Detection Dataset for Quadcopter Unmanned Aerial Vehicle Detection},
  author={Pawełczyk, Maciej and Wojtyra, Marek},
  journal={IEEE Access},
  volume={8},
  pages={174394--174409},
  year={2020},
  publisher={IEEE},
  doi={10.1109/ACCESS.2020.3026192}
}
```

**APA:**

Pawełczyk, M., & Wojtyra, M. (2020). Real World Object Detection Dataset for Quadcopter Unmanned Aerial Vehicle Detection. IEEE Access, 8, 174394-174409. https://doi.org/10.1109/ACCESS.2020.3026192

## More Information

For details, refer to the [original GitHub repository](https://github.com/Maciullo/DroneDetectionDataset).

## Dataset Card Authors

- me, [pathikg](https://huggingface.co/pathikg) 

## Dataset Card Contact

For questions, contact [pathikg](https://huggingface.co/pathikg)