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Roundabout Aerial Images Dataset

1547442213
Tagaerial, drones
Taskobject detection
Release YearMade in 2022
LicenseCC BY-NC-SA 4.0
Download13 GB

Introduction #

Enrique Puertas, Gonzalo De-Las-Heras, Javier Fernández-Andréset al.

The authors of the Roundabout Aerial Images for Vehicle Detection dataset have compiled a comprehensive collection containing 985,260 instances across 61,896 color images. These images consist of both real captures (15,474) and augmented data (46,422), all in JPG format. These images were sourced from eight distinct roundabouts, each representing various traffic flow conditions. The presented dataset contains real captures only.

Roundabout (scenes) Frames Car Truck Cycle Bus Empty
1 (00001) 1,996 34,558 0 4229 0 0
2 (00002) 514 743 0 0 0 157
3 (00003-00017) 1,795 4822 58 0 0 0
4 (00018-00033) 1,027 6615 0 0 0 0
5 (00034-00049) 1,261 2248 0 550 0 81
6 (00050-00052) 5,501 180,342 1420 120 1376 0
7 (00053) 2,036 5,789 562 0 226 92
8 (00054) 1,344 1,733 222 0 150 222
Total 15,474 236,850 2,262 4,899 1,752 552
Data augmentation (*) x4 x4 x4 x4 x4 x4
Total 61,896 947,400 9,048 19,596 7,008 2,208

(*) Data augmentation: Augmented data is available in the first version of the dataset.

Given the meticulous nature of image annotation, the authors employed an effective methodology to streamline the process which minimizes manual annotation efforts. This involves annotating a limited number of images to train Convolutional Neural Network (CNN) models capable of auto-annotating additional cases. Although these auto-annotations necessitate review, this approach reduces the need for extensive manual work. To further enhance dataset diversity without additional annotation, the authors applied data augmentation techniques, generating apparent new images.

image

The data collection process began by recording aerial videos of roundabouts and filtering out poor-quality segments. Captured using a DJI Mavic Mini 2 drone under varying conditions, these videos were shot during daylight from different heights. Heights between 100 and 120 meters were chosen to maintain the roundabout’s visibility with entrances and exits discernible. This height range corresponded to a ground sampling distance (GSD) of 6.67 to 8 cm per image pixel.

Subsequent steps involved manual annotation of frames extracted from the videos using a Python script and annotation software. Annotations were created in PASCAL VOC XML format, generating an individual XML file for each image. To enhance dataset volume, data augmentation was applied using the OpenCV library and Python scripting. Synthetic images were generated by employing various flips, including horizontal, vertical, and combined orientations. This is a technique widely used to create seemingly new examples with the least amount of work.

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Dataset LinkHomepageDataset LinkResearch Paper

Summary #

Roundabout Aerial Images for Vehicle Detection is a dataset for an object detection task. It is used in the vehicle detection domain.

The dataset consists of 15474 images with 245763 labeled objects belonging to 4 different classes including car, cycle, truck, and other: bus.

Images in the Roundabout Aerial Images dataset have bounding box annotations. There are 552 (4% of the total) unlabeled images (i.e. without annotations). There are no pre-defined train/val/test splits in the dataset. Additionally, the meta data about roundabout id (8 unique roundabouts), Drone’s lattitude and longitude, its height of exposure and height with zoom is provided. The dataset was released in 2022 by the Universidad Europea de Madrid, SICE Canada Inc., Toronto, and Universidad Francisco de Vitoria, Spain.

Dataset Poster

Explore #

Roundabout Aerial Images dataset has 15474 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.

OpenSample annotation mask from Roundabout Aerial ImagesSample image from Roundabout Aerial Images
OpenSample annotation mask from Roundabout Aerial ImagesSample image from Roundabout Aerial Images
OpenSample annotation mask from Roundabout Aerial ImagesSample image from Roundabout Aerial Images
OpenSample annotation mask from Roundabout Aerial ImagesSample image from Roundabout Aerial Images
OpenSample annotation mask from Roundabout Aerial ImagesSample image from Roundabout Aerial Images
OpenSample annotation mask from Roundabout Aerial ImagesSample image from Roundabout Aerial Images
OpenSample annotation mask from Roundabout Aerial ImagesSample image from Roundabout Aerial Images
OpenSample annotation mask from Roundabout Aerial ImagesSample image from Roundabout Aerial Images
OpenSample annotation mask from Roundabout Aerial ImagesSample image from Roundabout Aerial Images
OpenSample annotation mask from Roundabout Aerial ImagesSample image from Roundabout Aerial Images
OpenSample annotation mask from Roundabout Aerial ImagesSample image from Roundabout Aerial Images
OpenSample annotation mask from Roundabout Aerial ImagesSample image from Roundabout Aerial Images
OpenSample annotation mask from Roundabout Aerial ImagesSample image from Roundabout Aerial Images
OpenSample annotation mask from Roundabout Aerial ImagesSample image from Roundabout Aerial Images
OpenSample annotation mask from Roundabout Aerial ImagesSample image from Roundabout Aerial Images
OpenSample annotation mask from Roundabout Aerial ImagesSample image from Roundabout Aerial Images
OpenSample annotation mask from Roundabout Aerial ImagesSample image from Roundabout Aerial Images
OpenSample annotation mask from Roundabout Aerial ImagesSample image from Roundabout Aerial Images
OpenSample annotation mask from Roundabout Aerial ImagesSample image from Roundabout Aerial Images
OpenSample annotation mask from Roundabout Aerial ImagesSample image from Roundabout Aerial Images
OpenSample annotation mask from Roundabout Aerial ImagesSample image from Roundabout Aerial Images
OpenSample annotation mask from Roundabout Aerial ImagesSample image from Roundabout Aerial Images
OpenSample annotation mask from Roundabout Aerial ImagesSample image from Roundabout Aerial Images
OpenSample annotation mask from Roundabout Aerial ImagesSample image from Roundabout Aerial Images
OpenSample annotation mask from Roundabout Aerial ImagesSample image from Roundabout Aerial Images
OpenSample annotation mask from Roundabout Aerial ImagesSample image from Roundabout Aerial Images
OpenSample annotation mask from Roundabout Aerial ImagesSample image from Roundabout Aerial Images
OpenSample annotation mask from Roundabout Aerial ImagesSample image from Roundabout Aerial Images
OpenSample annotation mask from Roundabout Aerial ImagesSample image from Roundabout Aerial Images
OpenSample annotation mask from Roundabout Aerial ImagesSample image from Roundabout Aerial Images
👀
Have a look at 15474 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 4 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.

Search
Rows 1-4 of 4
Class
Images
Objects
Count on image
average
Area on image
average
car
rectangle
14739
236850
16.07
1.86%
cycle
rectangle
2406
4899
2.04
0.06%
truck
rectangle
1900
2262
1.19
0.53%
bus
rectangle
1506
1752
1.16
0.69%

Co-occurrence matrix #

Co-occurrence matrix is an extremely valuable tool that shows you the images for every pair of classes: how many images have objects of both classes at the same time. If you click any cell, you will see those images. We added the tooltip with an explanation for every cell for your convenience, just hover the mouse over a cell to preview the description.

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.

Search
Rows 1-4 of 4
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
car
rectangle
236850
0.12%
1.57%
0%
2px
0.19%
146px
13.52%
48px
4.46%
2px
0.1%
227px
11.82%
cycle
rectangle
4899
0.03%
0.1%
0%
8px
0.74%
44px
4.07%
29px
2.71%
8px
0.42%
69px
3.59%
truck
rectangle
2262
0.44%
2.04%
0%
5px
0.46%
202px
18.7%
85px
7.85%
4px
0.21%
262px
13.65%
bus
rectangle
1752
0.6%
1.68%
0.04%
23px
2.13%
211px
19.54%
106px
9.86%
31px
1.61%
269px
14.01%

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.

Spatial Heatmap

Objects #

Table contains all 245763 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.

Search
Rows 1-10 of 245763
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
car
rectangle
00001_frame000258_original.jpg
1080 x 1920
66px
6.11%
69px
3.59%
0.22%
2
car
rectangle
00001_frame000258_original.jpg
1080 x 1920
61px
5.65%
56px
2.92%
0.16%
3
car
rectangle
00001_frame000258_original.jpg
1080 x 1920
62px
5.74%
55px
2.86%
0.16%
4
car
rectangle
00001_frame000258_original.jpg
1080 x 1920
57px
5.28%
64px
3.33%
0.18%
5
car
rectangle
00001_frame000258_original.jpg
1080 x 1920
72px
6.67%
43px
2.24%
0.15%
6
car
rectangle
00001_frame000258_original.jpg
1080 x 1920
37px
3.43%
76px
3.96%
0.14%
7
car
rectangle
00001_frame000258_original.jpg
1080 x 1920
74px
6.85%
42px
2.19%
0.15%
8
car
rectangle
00001_frame000258_original.jpg
1080 x 1920
74px
6.85%
54px
2.81%
0.19%
9
car
rectangle
00001_frame000258_original.jpg
1080 x 1920
72px
6.67%
38px
1.98%
0.13%
10
car
rectangle
00001_frame000258_original.jpg
1080 x 1920
62px
5.74%
61px
3.18%
0.18%

License #

Roundabout Aerial Images for Vehicle Detection is under CC BY-NC-SA 4.0 license.

Source

Citation #

If you make use of the Roundabout Aerial Images data, please cite the following reference:

Puertas, E.; De-Las-Heras, G.; Fernández-Andrés, J.; Sánchez-Soriano, J. Dataset: Roundabout Aerial Images for Vehicle Detection. Data 2022, 7, 47. https://doi.org/10.3390/data7040047 

Source

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

@misc{ visualization-tools-for-roundabout-aerial-images-for-vehicle-detection-dataset,
  title = { Visualization Tools for Roundabout Aerial Images Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/roundabout-aerial-images-for-vehicle-detection } },
  url = { https://datasetninja.com/roundabout-aerial-images-for-vehicle-detection },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { mar },
  note = { visited on 2024-03-05 },
}

Download #

Dataset Roundabout Aerial Images 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='Roundabout Aerial Images', 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.

The data in original format can be downloaded here.

. . .

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