Drone Dataset (UAV) - Dataset Ninja

Introduction #

Released 2019-09-26 ·Mehdi Özel

Authors introduce the Drone Dataset (UAV), a comprehensive collection of 1,359 images, all belonging to a single class: drone. This dataset is meticulously split into train and valid subsets, comprising 1,012 and 347 images, respectively. The primary purpose of creating this dataset is to facilitate the training of Unmanned Aerial Vehicles (UAVs) in the critical tasks of guidance and collision avoidance as they navigate through the increasingly crowded skies alongside other UAVs.

This dataset collected by author in Istanbul (Turkey) for a UAV Competition. Most Images downloaded from Google and Yandex with image scrapers. Also, some YouTube videos used to scrap some images.

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Homepage

GitHub

Summary #

Drone Dataset (UAV) is a dataset for an object detection task. Possible applications of the dataset could be in the drone inspection domain.

The dataset consists of 1359 images with 1486 labeled objects belonging to 1 single class (drone).

Images in the Drone Dataset (UAV) dataset have bounding box annotations. All images are labeled (i.e. with annotations). There are 2 splits in the dataset: train (1012 images) and valid (347 images). The dataset was released in 2019.

Explore #

Drone Dataset (UAV) dataset has 1359 images. Click on one of the examples below or open "Explore" tool anytime you need to view dataset images with annotations. This tool has extended visualization capabilities like zoom, translation, objects table, custom filters and more. Hover the mouse over the images to hide or show annotations.

Sample annotation mask from Drone Dataset (UAV)

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Have a look at 1359 images

View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 1 annotation classes in the dataset. Find the general statistics and balances for every class in the table below. Click any row to preview images that have labels of the selected class. Sort by column to find the most rare or prevalent classes.

Rows 1-1 of 1

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
drone➔ rectangle	1359	1486	1.09	37.75%

Images #

Explore every single image in the dataset with respect to the number of annotations of each class it has. Click a row to preview selected image. Sort by any column to find anomalies and edge cases. Use horizontal scroll if the table has many columns for a large number of classes in the dataset.

Object distribution #

Interactive heatmap chart for every class with object distribution shows how many images are in the dataset with a certain number of objects of a specific class. Users can click cell and see the list of all corresponding images.

Class sizes #

The table below gives various size properties of objects for every class. Click a row to see the image with annotations of the selected class. Sort columns to find classes with the smallest or largest objects or understand the size differences between classes.

Rows 1-1 of 1

Class	Object count	Avg area	Max area	Min area	Min height	Min height	Max height	Max height	Avg height	Avg height	Min width	Min width	Max width	Max width
drone rectangle	1486	34.57%	98.81%	0.02%	14px	1.94%	2364px	99.91%	312px	47.68%	15px	1.17%	4975px	100%

Spatial Heatmap #

The heatmaps below give the spatial distributions of all objects for every class. These visualizations provide insights into the most probable and rare object locations on the image. It helps analyze objects' placements in a dataset.

Objects #

Table contains all 1486 objects. Click a row to preview an image with annotations, and use search or pagination to navigate. Sort columns to find outliers in the dataset.

Rows 1-10 of 1486

Object ID ㅤ	Class ㅤ	Image name click row to open	Image size height x width	Height ㅤ	Height ㅤ	Width ㅤ	Width ㅤ	Area ㅤ
1➔	drone rectangle	0219.jpg	2001 x 3101	1155px	57.72%	2041px	65.82%	37.99%
2➔	drone rectangle	0004.jpg	1334 x 2000	643px	48.2%	1471px	73.55%	35.45%
3➔	drone rectangle	0290.jpg	550 x 900	311px	56.55%	632px	70.22%	39.71%
4➔	drone rectangle	0305.jpg	540 x 800	457px	84.63%	745px	93.12%	78.81%
5➔	drone rectangle	foto05047.jpg.png	720 x 1280	291px	40.42%	550px	42.97%	17.37%
6➔	drone rectangle	foto01306.jpg.png	720 x 1280	352px	48.89%	487px	38.05%	18.6%
7➔	drone rectangle	0028.jpg	183 x 275	100px	54.64%	210px	76.36%	41.73%
8➔	drone rectangle	0028.jpg	183 x 275	36px	19.67%	102px	37.09%	7.3%
9➔	drone rectangle	0056.jpg	225 x 225	136px	60.44%	208px	92.44%	55.88%
10➔	drone rectangle	0312.jpg	604 x 907	191px	31.62%	419px	46.2%	14.61%

License #

Drone Dataset (UAV) is under MIT license.

Source

Citation #

If you make use of the Drone Dataset (UAV) data, please cite the following reference:

@dataset{Drone Dataset (UAV),
  author={Mehdi Özel},
  title={Drone Dataset (UAV)},
  year={2019},
  url={https://www.kaggle.com/datasets/dasmehdixtr/drone-dataset-uav?select=drone_dataset_yolo}
}

Source

If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:

@misc{ visualization-tools-for-drone-dataset-uav-dataset,
  title = { Visualization Tools for Drone Dataset (UAV) Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/drone-dataset-uav } },
  url = { https://datasetninja.com/drone-dataset-uav },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { may },
  note = { visited on 2024-05-16 },
}

Download #

Dataset Drone Dataset (UAV) can be downloaded in Supervisely format:

As an alternative, it can be downloaded with dataset-tools package:

pip install --upgrade dataset-tools

… using following python code:

import dataset_tools as dtools

dtools.download(dataset='Drone Dataset (UAV)', dst_dir='~/dataset-ninja/')

Make sure not to overlook the python code example available on the Supervisely Developer Portal. It will give you a clear idea of how to effortlessly work with the downloaded dataset.

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