6.3 KiB

Raw Blame History

comments	description	keywords
true	Delve into DOTA, an Oriented Bounding Box (OBB) aerial imagery dataset with 1.7 million instances and 11,268 images.	DOTA v1, DOTA v1.5, DOTA v2, object detection, aerial images, computer vision, deep learning, annotations, oriented bounding boxes, OBB

DOTA Dataset with OBB

DOTA stands as a specialized dataset, emphasizing object detection in aerial images. Originating from the DOTA series of datasets, it offers annotated images capturing a diverse array of aerial scenes with Oriented Bounding Boxes (OBB).

Key Features

Collection from various sensors and platforms, with image sizes ranging from 800 × 800 to 20,000 × 20,000 pixels.
Features more than 1.7M Oriented Bounding Boxes across 18 categories.
Encompasses multiscale object detection.
Instances are annotated by experts using arbitrary (8 d.o.f.) quadrilateral, capturing objects of different scales, orientations, and shapes.

Dataset Versions

DOTA-v1.0

Contains 15 common categories.
Comprises 2,806 images with 188,282 instances.
Split ratios: 1/2 for training, 1/6 for validation, and 1/3 for testing.

DOTA-v1.5

Incorporates the same images as DOTA-v1.0.
Very small instances (less than 10 pixels) are also annotated.
Addition of a new category: "container crane".
A total of 403,318 instances.
Released for the DOAI Challenge 2019 on Object Detection in Aerial Images.

DOTA-v2.0

Collections from Google Earth, GF-2 Satellite, and other aerial images.
Contains 18 common categories.
Comprises 11,268 images with a whopping 1,793,658 instances.
New categories introduced: "airport" and "helipad".
Image splits:
- Training: 1,830 images with 268,627 instances.
- Validation: 593 images with 81,048 instances.
- Test-dev: 2,792 images with 353,346 instances.
- Test-challenge: 6,053 images with 1,090,637 instances.

Dataset Structure

DOTA exhibits a structured layout tailored for OBB object detection challenges:

Images: A vast collection of high-resolution aerial images capturing diverse terrains and structures.
Oriented Bounding Boxes: Annotations in the form of rotated rectangles encapsulating objects irrespective of their orientation, ideal for capturing objects like airplanes, ships, and buildings.

Applications

DOTA serves as a benchmark for training and evaluating models specifically tailored for aerial image analysis. With the inclusion of OBB annotations, it provides a unique challenge, enabling the development of specialized object detection models that cater to aerial imagery's nuances.

Dataset YAML

Typically, datasets incorporate a YAML (Yet Another Markup Language) file detailing the dataset's configuration. For DOTA v1 and DOTA v1.5, Ultralytics provides DOTAv1.yaml and DOTAv1.5.yaml files. For additional details on these as well as DOTA v2 please consult DOTA's official repository and documentation.

!!! Example "DOTAv1.yaml"

```yaml
--8<-- "ultralytics/cfg/datasets/DOTAv1.yaml"
```

Split DOTA images

To train DOTA dataset, We split original DOTA images with high-resolution into images with 1024x1024 resolution in multi-scale way.

!!! Example "Split images"

=== "Python"

    ```python
    from ultralytics.data.split_dota import split_trainval, split_test

    # split train and val set, with labels.
    split_trainval(
        data_root='path/to/DOTAv1.0/',
        save_dir='path/to/DOTAv1.0-split/',
        rates=[0.5, 1.0, 1.5],    # multi-scale
        gap=500
    )
    # split test set, without labels.
    split_test(
        data_root='path/to/DOTAv1.0/',
        save_dir='path/to/DOTAv1.0-split/',
        rates=[0.5, 1.0, 1.5],    # multi-scale
        gap=500
    )
    ```

Usage

To train a model on the DOTA v1 dataset, you can utilize the following code snippets. Always refer to your model's documentation for a thorough list of available arguments.

!!! Warning

Please note that all images and associated annotations in the DOTAv1 dataset can be used for academic purposes, but commercial use is prohibited. Your understanding and respect for the dataset creators' wishes are greatly appreciated!

!!! Example "Train Example"

=== "Python"

    ```python
    from ultralytics import YOLO

    # Create a new YOLOv8n-OBB model from scratch
    model = YOLO('yolov8n-obb.yaml')

    # Train the model on the DOTAv2 dataset
    results = model.train(data='DOTAv1.yaml', epochs=100, imgsz=640)
    ```

=== "CLI"

    ```bash
    # Train a new YOLOv8n-OBB model on the DOTAv2 dataset
    yolo detect train data=DOTAv1.yaml model=yolov8n.pt epochs=100 imgsz=640
    ```

Sample Data and Annotations

Having a glance at the dataset illustrates its depth:

DOTA examples: This snapshot underlines the complexity of aerial scenes and the significance of Oriented Bounding Box annotations, capturing objects in their natural orientation.

The dataset's richness offers invaluable insights into object detection challenges exclusive to aerial imagery.

Citations and Acknowledgments

For those leveraging DOTA in their endeavors, it's pertinent to cite the relevant research papers:

!!! Quote ""

=== "BibTeX"

    ```bibtex
    @article{9560031,
      author={Ding, Jian and Xue, Nan and Xia, Gui-Song and Bai, Xiang and Yang, Wen and Yang, Michael and Belongie, Serge and Luo, Jiebo and Datcu, Mihai and Pelillo, Marcello and Zhang, Liangpei},
      journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
      title={Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges},
      year={2021},
      volume={},
      number={},
      pages={1-1},
      doi={10.1109/TPAMI.2021.3117983}
    }
    ```

A special note of gratitude to the team behind the DOTA datasets for their commendable effort in curating this dataset. For an exhaustive understanding of the dataset and its nuances, please visit the official DOTA website.

6.3 KiB Raw Blame History