The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 153, in compute
                  compute_split_names_from_info_response(
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 125, in compute_split_names_from_info_response
                  config_info_response = get_previous_step_or_raise(kind="config-info", dataset=dataset, config=config)
                File "/src/libs/libcommon/src/libcommon/simple_cache.py", line 591, in get_previous_step_or_raise
                  raise CachedArtifactError(
              libcommon.simple_cache.CachedArtifactError: The previous step failed.
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 499, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/folder_based_builder/folder_based_builder.py", line 186, in _split_generators
                  raise ValueError("`file_name` must be present as dictionary key in metadata files")
              ValueError: `file_name` must be present as dictionary key in metadata files
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 71, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 572, in get_dataset_split_names
                  info = get_dataset_config_info(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 504, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

DDOS: The Drone Depth and Obstacle Segmentation Dataset

The Drone Depth and Obstacle Segmentation (DDOS) dataset comprises synthetic aerial images captured by drones, along with corresponding depth maps and pixel-wise semantic segmentation masks. DDOS is purpose-built to support research and development in computer vision, focusing on tasks such as depth estimation and obstacle segmentation from aerial imagery. Emphasizing the detection of thin structures like wires and effective navigation in diverse weather conditions, DDOS serves as a valuable resource for advancing algorithms in autonomous drone technology.


Data Structure

DDOS is organised as follows:

  • Data Splits:

    • Train: Contains 300 flights with a total of 30k images for training.
    • Validation: Contains 20 flights with a total of 2k images for validation during model development.
    • Test: Contains 20 flights with a total of 2k images for the final evaluation of the trained model.
  • Environments:

    • Neighbourhood: Contains data captured in urban and residential environments.
    • Park: Contains data captured in park and natural environments.
  • Flights:

    • Each flight is represented by a unique flight ID and is contained within the corresponding environment directory.
  • Data for Each Flight:

    • Image: Contains RGB images captured by the drone camera.
    • Depth: Contains depth maps representing the distance of objects from the camera. These maps are saved as uint16 PNG images, where pixel values range from 0 to 65535, representing distances from 0 to 100 meters linearly.
    • Segmentation: Contains pixel-wise segmentation masks for semantic segmentation. Classes, as well as their corresponding mappings, are mentioned below.
    • Flow: Contains optical flow data representing the apparent motion of objects between consecutive frames.
    • Surface Normal: Contains surface normal maps representing the orientation of object surfaces.

Overview of file structure:

data/
β”œβ”€β”€ train/
β”‚   β”œβ”€β”€ neighbourhood/
β”‚   β”‚   β”œβ”€β”€ 0/
β”‚   β”‚   β”‚   β”œβ”€β”€ depth/
β”‚   β”‚   β”‚   β”‚   β”œβ”€β”€ 0.png
β”‚   β”‚   β”‚   β”‚   β”œβ”€β”€ ...
β”‚   β”‚   β”‚   β”‚   └── 99.png
β”‚   β”‚   β”‚   β”œβ”€β”€ flow/
β”‚   β”‚   β”‚   β”‚   β”œβ”€β”€ 0.png
β”‚   β”‚   β”‚   β”‚   β”œβ”€β”€ ...
β”‚   β”‚   β”‚   β”‚   └── 99.png
β”‚   β”‚   β”‚   β”œβ”€β”€ image/
β”‚   β”‚   β”‚   β”‚   β”œβ”€β”€ 0.png
β”‚   β”‚   β”‚   β”‚   β”œβ”€β”€ ...
β”‚   β”‚   β”‚   β”‚   └── 99.png
β”‚   β”‚   β”‚   β”œβ”€β”€ segmentation/
β”‚   β”‚   β”‚   β”‚   β”œβ”€β”€ 0.png
β”‚   β”‚   β”‚   β”‚   β”œβ”€β”€ ...
β”‚   β”‚   β”‚   β”‚   └── 99.png
β”‚   β”‚   β”‚   β”œβ”€β”€ surfacenormals/
β”‚   β”‚   β”‚   β”‚   β”œβ”€β”€ 0.png
β”‚   β”‚   β”‚   β”‚   β”œβ”€β”€ ...
β”‚   β”‚   β”‚   β”‚   └── 99.png
β”‚   β”‚   β”‚   β”œβ”€β”€ metadata.csv
β”‚   β”‚   β”‚   └── weather.csv
β”‚   β”‚   β”œβ”€β”€ ...
β”‚   β”‚   └── 249/
β”‚   β”‚       └── ...
β”‚   └── park/
β”‚       β”œβ”€β”€ 0/
β”‚       β”‚   β”œβ”€β”€ depth/
β”‚       β”‚   β”‚   └── ...
β”‚       β”‚   β”œβ”€β”€ flow/
β”‚       β”‚   β”‚   └── ...
β”‚       β”‚   β”œβ”€β”€ image/
β”‚       β”‚   β”‚   └── ...
β”‚       β”‚   β”œβ”€β”€ segmentation/
β”‚       β”‚   β”‚   └── ...
β”‚       β”‚   β”œβ”€β”€ surfacenormals/
β”‚       β”‚   β”‚   └── ...
β”‚       β”‚   β”œβ”€β”€ metadata.csv
β”‚       β”‚   └── weather.csv
β”‚       β”œβ”€β”€ ...
β”‚       └── 49/
β”‚           └── ...
β”œβ”€β”€ validation/
β”‚   └── ...
└── test/
    └── ...

Additional Information

Class Mapping: The segmentation masks use the following class labels for obstacle segmentation:

CLASS_MAPPING = {
    'ultra_thin': 255,
    'thin_structures': 240,
    'small_mesh': 220,
    'large_mesh': 200,
    'trees': 180,
    'buildings': 160,
    'vehicles': 140,
    'animals': 100,
    'other': 80
}

Metadata: The dataset contains metadata, such as coordinates, pose, acceleration, weather conditions and camera parameters, which provide valuable contextual information about each flight.


Dataset Usage

  • Data Loading: To load and use the DDOS dataset in your projects, you can refer to the official PyTorch data loading tutorial: PyTorch Data Loading Tutorial. This tutorial will guide you through the process of loading data, creating data loaders, and preparing the dataset for training or evaluation using PyTorch.

  • Respect the Data Splits: Please ensure that the testing data is not used for validation. Mixing these datasets could lead to inaccurate assessments of model performance. Maintaining separate datasets for testing and validation helps ensure reliable evaluation and accurate reporting of results.


License

DDOS is openly licensed under CC BY-NC 4.0


Citation

If you use DDOS in your research or projects, please cite our paper:

@article{kolbeinsson2023ddos,
  title={{DDOS}: The Drone Depth and Obstacle Segmentation Dataset},
  author={Benedikt Kolbeinsson and Krystian Mikolajczyk},
  journal={arXiv preprint arXiv:2312.12494},
  year={2023}
}
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