Introduction #
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.
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.
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.
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.
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.
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 #
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}
}
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 = { dec },
note = { visited on 2024-12-07 },
}
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.
Disclaimer #
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