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Military Aircraft Detection Dataset

12008431711
Tagsecurity
Taskobject detection
Release YearMade in 2020
Licenseunknown

Introduction #

T Nakamura

Authors introduce the Military Aircraft Detection Dataset, a comprehensive dataset designed for object detection of military aircraft. This dataset features bounding boxes in PASCAL VOC format (xmin, ymin, xmax, ymax) and includes images of 43 distinct aircraft types, such as A-10, F-35, Su-57, and more. The dataset, comprising 12,008 images in total, was sourced from Wikimedia Commons and Google Image Search, making it a valuable resource for training and evaluating object detection models for military aircraft recognition task.

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Dataset LinkHomepage

Summary #

Military Aircraft Detection is a dataset for an object detection task. Possible applications of the dataset could be in the security industry.

The dataset consists of 12008 images with 19270 labeled objects belonging to 43 different classes including F16, F15, F35, and other: F18, C2, C130, US2, V22, B1, A10, F4, C17, B2, F22, EF2000, B52, C5, JAS39, Rafale, A400M, E2, Vulcan, MQ9, AV8B, F14, Tornado, Be200, J20, RQ4, U2, Su34, SR71, Mirage2000, Mig31, Tu160, AG600, Su57, F117, Tu95, P3, XB70, E7, and YF23.

Images in the Military Aircraft Detection dataset have bounding box annotations. All images are labeled (i.e. with annotations). There are no pre-defined train/val/test splits in the dataset. The dataset was released in 2020.

Here is a visualized example for randomly selected sample classes:

Explore #

Military Aircraft Detection dataset has 12008 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 Military Aircraft DetectionSample image from Military Aircraft Detection
OpenSample annotation mask from Military Aircraft DetectionSample image from Military Aircraft Detection
OpenSample annotation mask from Military Aircraft DetectionSample image from Military Aircraft Detection
OpenSample annotation mask from Military Aircraft DetectionSample image from Military Aircraft Detection
OpenSample annotation mask from Military Aircraft DetectionSample image from Military Aircraft Detection
OpenSample annotation mask from Military Aircraft DetectionSample image from Military Aircraft Detection
OpenSample annotation mask from Military Aircraft DetectionSample image from Military Aircraft Detection
OpenSample annotation mask from Military Aircraft DetectionSample image from Military Aircraft Detection
OpenSample annotation mask from Military Aircraft DetectionSample image from Military Aircraft Detection
OpenSample annotation mask from Military Aircraft DetectionSample image from Military Aircraft Detection
OpenSample annotation mask from Military Aircraft DetectionSample image from Military Aircraft Detection
OpenSample annotation mask from Military Aircraft DetectionSample image from Military Aircraft Detection
πŸ‘€
Have a look at 12008 images
Because of dataset's license preview is limited to 12 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 43 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 1-10 of 43
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
F16βž”
rectangle
706
1155
1.64
24.83%
F15βž”
rectangle
668
1116
1.67
25.5%
F35βž”
rectangle
660
976
1.48
18.37%
F18βž”
rectangle
574
1163
2.03
24.35%
C2βž”
rectangle
569
715
1.26
32.89%
C130βž”
rectangle
563
814
1.45
19.38%
US2βž”
rectangle
517
570
1.1
19.85%
V22βž”
rectangle
487
734
1.51
19.23%
B1βž”
rectangle
380
535
1.41
22.24%
A10βž”
rectangle
372
567
1.52
17.59%

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 1-10 of 43
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
F18
rectangle
1163
12.61%
99.94%
0.01%
10px
0.76%
2725px
99.96%
310px
22.57%
14px
1.09%
5317px
99.98%
F16
rectangle
1155
15.97%
99.94%
0.02%
10px
1.22%
4095px
99.98%
397px
27.64%
17px
1.32%
5840px
99.98%
F15
rectangle
1116
15.66%
99.96%
0.01%
8px
1.12%
4095px
99.98%
340px
26.12%
12px
1.2%
6015px
99.98%
F35
rectangle
976
12.67%
99.94%
0.02%
9px
1.12%
4095px
99.98%
320px
23.42%
18px
1.3%
4458px
99.98%
C130
rectangle
814
13.68%
99.94%
0%
9px
0.44%
4095px
99.98%
326px
22.95%
22px
0.76%
6047px
99.98%
V22
rectangle
734
13.17%
99.88%
0.02%
10px
0.97%
2760px
99.93%
282px
22.65%
17px
1.42%
5475px
99.98%
C2
rectangle
715
26.76%
99.86%
0.05%
12px
1.76%
3890px
99.92%
352px
36.96%
15px
2.93%
6047px
99.98%
US2
rectangle
570
18.29%
99.93%
0.04%
13px
1.3%
3909px
99.96%
272px
27.4%
25px
2.08%
5817px
99.98%
A10
rectangle
567
11.77%
99.95%
0.03%
12px
1.33%
3187px
99.97%
319px
21.94%
25px
1.22%
4927px
99.98%
F22
rectangle
552
12.14%
99.94%
0.01%
12px
0.55%
3858px
99.96%
333px
23.85%
31px
0.95%
6876px
99.99%

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 19270 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 19270
Object ID
γ…€
Class
γ…€
Image name
click row to open
Image size
height x width
Height
γ…€
Height
γ…€
Width
γ…€
Width
γ…€
Area
γ…€
1βž”
F18
rectangle
d26599941d61aa42ea763e9abf4818c7.jpg
2048 x 2048
1593px
77.78%
1108px
54.1%
42.08%
2βž”
J20
rectangle
c5daa1bd8440d29c8f4027bef9017ac1.jpg
540 x 960
118px
21.85%
73px
7.6%
1.66%
3βž”
J20
rectangle
c5daa1bd8440d29c8f4027bef9017ac1.jpg
540 x 960
80px
14.81%
65px
6.77%
1%
4βž”
Mig31
rectangle
cc4aa960860c8da40240d319ea6f5f76.jpg
1602 x 1068
577px
36.02%
897px
83.99%
30.25%
5βž”
Mig31
rectangle
cc4aa960860c8da40240d319ea6f5f76.jpg
1602 x 1068
443px
27.65%
683px
63.95%
17.68%
6βž”
Vulcan
rectangle
8f5afc820a07f70a96bf34ee43abd30d.jpg
665 x 1024
294px
44.21%
1023px
99.9%
44.17%
7βž”
AV8B
rectangle
1fb5e5615223d27cd04be61033a8b06d.jpg
2048 x 1710
298px
14.55%
554px
32.4%
4.71%
8βž”
EF2000
rectangle
e77db44803b33494f10221413b3274d2.jpg
1281 x 1920
436px
34.04%
1301px
67.76%
23.06%
9βž”
EF2000
rectangle
e77db44803b33494f10221413b3274d2.jpg
1281 x 1920
513px
40.05%
1477px
76.93%
30.81%
10βž”
F16
rectangle
7f865c2a1fb9a5c8ba1ce308d52e1ed9.jpg
1333 x 2000
463px
34.73%
983px
49.15%
17.07%

License #

License is unknown for the Military Aircraft Detection dataset.

Source

Citation #

If you make use of the Military Aircraft Detection data, please cite the following reference:

@dataset{Military Aircraft Detection,
  author={T Nakamura},
  title={Military Aircraft Detection},
  year={2020},
  url={https://www.kaggle.com/datasets/a2015003713/militaryaircraftdetectiondataset}
}

Source

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

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

Download #

Please visit dataset homepage to download the data.

. . .

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