Dataset Ninja LogoDataset Ninja:

AFO Dataset

364091920
Tagsafety, aerial, drones
Taskobject detection
Release YearMade in 2021
LicenseCC BY-NC-SA 3.0 IGO
Download4 GB

Introduction #

Jan Gąsienica-Józkowy, Mateusz Knapik, Boguslaw Cyganek

The authors of the AFO: Aerial Dataset of Floating Objects present a comprehensive study focusing on leveraging deep learning architectures for marine search and rescue operations involving object detection. This dataset consists of 3647 images, each manually annotated with nearly 40,000 labeled objects.

Highlighting the significance of the study, the authors discuss the challenges associated with search and rescue operations, emphasizing the difficulties in localizing missing individuals and objects. They emphasize the potential benefits of incorporating unmanned aerial vehicles, artificial intelligence, and computer vision technologies to enhance rescue missions. While these technologies offer promising assistance, the authors underscore the scarcity of object detection systems based on neural networks in SAR applications, partly due to the lack of sufficiently large training datasets.

To address this limitation, the authors present the AFO dataset, which is meticulously curated for the problem of detecting small objects in marine environments. The dataset is divided into three subsets: train, test, and validation. The authors also elaborate on the considerations for data selection, including altitude and angle restrictions for video recordings, as well as the inclusion of various environmental and weather conditions in the dataset.

Each class here belongs to one of three dataset versions (depending on the goal of the CV task): 6categories version (classes human, wind/sup-board, kayak, boat, bouy, sailboat) to check how accurately detectors can detect humans vs. other floating objects (a large data imbalance ); 2categories version (classes small_obj, large_obj) to the task of searching for missing people (small objects) and boats (large objects) (slightly more balanced ); 1category version (class object) which marks all bounding boxes as one class to reflect the fact that usually during search and rescue operations at the sea, finding any object of a human origin can be significant.

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Dataset LinkHomepageDataset LinkResearch Paper

Summary #

AFO: Aerial Dataset of Floating Objects is a dataset for an object detection task. It is used in the search and rescue (SAR) industry.

The dataset consists of 3640 images with 119973 labeled objects belonging to 9 different classes including object, small_obj, human, and other: large_obj, wind/sup-board, kayak, boat, bouy, and sailboat.

Images in the AFO dataset have bounding box annotations. There are 728 (20% of the total) unlabeled images (i.e. without annotations). There are 3 splits in the dataset: train (2787 images), test (514 images), and validation (339 images). The dataset was released in 2021 by the AGH University of Science and Technology, Poland.

Dataset Poster

Explore #

AFO dataset has 3640 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 AFOSample image from AFO
OpenSample annotation mask from AFOSample image from AFO
OpenSample annotation mask from AFOSample image from AFO
OpenSample annotation mask from AFOSample image from AFO
OpenSample annotation mask from AFOSample image from AFO
OpenSample annotation mask from AFOSample image from AFO
OpenSample annotation mask from AFOSample image from AFO
OpenSample annotation mask from AFOSample image from AFO
OpenSample annotation mask from AFOSample image from AFO
OpenSample annotation mask from AFOSample image from AFO
OpenSample annotation mask from AFOSample image from AFO
OpenSample annotation mask from AFOSample image from AFO
OpenSample annotation mask from AFOSample image from AFO
OpenSample annotation mask from AFOSample image from AFO
OpenSample annotation mask from AFOSample image from AFO
OpenSample annotation mask from AFOSample image from AFO
OpenSample annotation mask from AFOSample image from AFO
OpenSample annotation mask from AFOSample image from AFO
OpenSample annotation mask from AFOSample image from AFO
OpenSample annotation mask from AFOSample image from AFO
OpenSample annotation mask from AFOSample image from AFO
OpenSample annotation mask from AFOSample image from AFO
OpenSample annotation mask from AFOSample image from AFO
OpenSample annotation mask from AFOSample image from AFO
OpenSample annotation mask from AFOSample image from AFO
OpenSample annotation mask from AFOSample image from AFO
OpenSample annotation mask from AFOSample image from AFO
OpenSample annotation mask from AFOSample image from AFO
OpenSample annotation mask from AFOSample image from AFO
OpenSample annotation mask from AFOSample image from AFO
👀
Have a look at 3640 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 9 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 1-9 of 9
Class
Images
Objects
Count on image
average
Area on image
average
object
rectangle
2912
39991
13.73
1.89%
small_obj
rectangle
2124
33761
15.9
1.42%
human
rectangle
2107
33174
15.74
1.43%
large_obj
rectangle
1931
6230
3.23
1.45%
wind/sup-board
rectangle
1025
3922
3.83
1.35%
kayak
rectangle
546
1446
2.65
0.87%
boat
rectangle
339
702
2.07
1.46%
bouy
rectangle
177
587
3.32
0.06%
sailboat
rectangle
131
160
1.22
3.56%

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 1-9 of 9
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
object
rectangle
39991
0.15%
21.31%
0%
6px
0.28%
1164px
80.83%
92px
4.48%
11px
0.42%
1256px
49.06%
small_obj
rectangle
33761
0.09%
1.1%
0%
10px
0.69%
308px
15.28%
80px
3.8%
11px
0.42%
293px
9.53%
human
rectangle
33174
0.09%
1.1%
0.01%
16px
0.79%
308px
15.28%
81px
3.84%
14px
0.42%
293px
9.53%
large_obj
rectangle
6230
0.46%
21.31%
0%
6px
0.28%
1164px
80.83%
155px
8.15%
19px
0.74%
1256px
49.06%
wind/sup-board
rectangle
3922
0.36%
0.92%
0%
6px
0.28%
345px
15.97%
164px
7.61%
32px
0.83%
382px
9.95%
kayak
rectangle
1446
0.33%
1.2%
0.02%
18px
1.18%
254px
16.6%
119px
7.8%
35px
1.29%
222px
8.16%
boat
rectangle
702
0.72%
16.19%
0.02%
22px
1.53%
657px
34.38%
130px
8.1%
19px
0.74%
1256px
49.06%
bouy
rectangle
587
0.02%
0.07%
0%
10px
0.69%
75px
3.47%
37px
1.75%
11px
0.43%
86px
2.24%
sailboat
rectangle
160
2.96%
21.31%
0.14%
130px
9.03%
1164px
80.83%
360px
24.73%
40px
1.56%
675px
26.37%

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 119973 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 119973
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
object
rectangle
r3_255.jpg
1530 x 2720
107px
6.99%
100px
3.68%
0.26%
2
object
rectangle
r3_255.jpg
1530 x 2720
105px
6.86%
76px
2.79%
0.19%
3
object
rectangle
r3_255.jpg
1530 x 2720
84px
5.49%
54px
1.99%
0.11%
4
large_obj
rectangle
r3_255.jpg
1530 x 2720
107px
6.99%
100px
3.68%
0.26%
5
large_obj
rectangle
r3_255.jpg
1530 x 2720
105px
6.86%
76px
2.79%
0.19%
6
large_obj
rectangle
r3_255.jpg
1530 x 2720
84px
5.49%
54px
1.99%
0.11%
7
kayak
rectangle
r3_255.jpg
1530 x 2720
107px
6.99%
100px
3.68%
0.26%
8
kayak
rectangle
r3_255.jpg
1530 x 2720
105px
6.86%
76px
2.79%
0.19%
9
kayak
rectangle
r3_255.jpg
1530 x 2720
84px
5.49%
54px
1.99%
0.11%
10
object
rectangle
i3_137.jpg
1440 x 2560
584px
40.56%
357px
13.95%
5.66%

License #

AFO: Aerial Dataset of Floating Objects is under CC BY-NC-SA 3.0 IGO license.

Source

Citation #

If you make use of the AFO data, please cite the following reference:

@article{article,
    author = {Gąsienica-Józkowy, Jan and Knapik, Mateusz and Cyganek, Boguslaw},
    year = {2021},
    month = {01},
    pages = {1-15},
    title = {An ensemble deep learning method with optimized weights for drone-based water rescue and surveillance},
    journal = {Integrated Computer-Aided Engineering},
    doi = {10.3233/ICA-210649}
}

Source

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

@misc{ visualization-tools-for-afo-dataset,
  title = { Visualization Tools for AFO Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/afo } },
  url = { https://datasetninja.com/afo },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { feb },
  note = { visited on 2024-02-24 },
}

Download #

Dataset AFO 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='AFO', 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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