Introduction #
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.
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.
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.
Class ã…¤ | Images ã…¤ | Objects ã…¤ | Count on image average | Area on image average |
---|---|---|---|---|
F16âž” rectangle | 706 | 1155 | 1.64 | 24.83% |
F15âž” rectangle | 668 | 1116 | 1.67 | 25.5% |
F35âž” rectangle | 660 | 976 | 1.48 | 18.37% |
F18âž” rectangle | 574 | 1163 | 2.03 | 24.35% |
C2âž” rectangle | 569 | 715 | 1.26 | 32.89% |
C130âž” rectangle | 563 | 814 | 1.45 | 19.38% |
US2âž” rectangle | 517 | 570 | 1.1 | 19.85% |
V22âž” rectangle | 487 | 734 | 1.51 | 19.23% |
B1âž” rectangle | 380 | 535 | 1.41 | 22.24% |
A10âž” 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.
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.
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.
Object ID ã…¤ | Class ã…¤ | Image name click row to open | Image size height x width | Height ã…¤ | Height ã…¤ | Width ã…¤ | Width ã…¤ | Area ã…¤ |
---|---|---|---|---|---|---|---|---|
1âž” | F18 rectangle | d26599941d61aa42ea763e9abf4818c7.jpg | 2048 x 2048 | 1593px | 77.78% | 1108px | 54.1% | 42.08% |
2âž” | J20 rectangle | c5daa1bd8440d29c8f4027bef9017ac1.jpg | 540 x 960 | 118px | 21.85% | 73px | 7.6% | 1.66% |
3âž” | J20 rectangle | c5daa1bd8440d29c8f4027bef9017ac1.jpg | 540 x 960 | 80px | 14.81% | 65px | 6.77% | 1% |
4âž” | Mig31 rectangle | cc4aa960860c8da40240d319ea6f5f76.jpg | 1602 x 1068 | 577px | 36.02% | 897px | 83.99% | 30.25% |
5âž” | Mig31 rectangle | cc4aa960860c8da40240d319ea6f5f76.jpg | 1602 x 1068 | 443px | 27.65% | 683px | 63.95% | 17.68% |
6âž” | Vulcan rectangle | 8f5afc820a07f70a96bf34ee43abd30d.jpg | 665 x 1024 | 294px | 44.21% | 1023px | 99.9% | 44.17% |
7âž” | AV8B rectangle | 1fb5e5615223d27cd04be61033a8b06d.jpg | 2048 x 1710 | 298px | 14.55% | 554px | 32.4% | 4.71% |
8âž” | EF2000 rectangle | e77db44803b33494f10221413b3274d2.jpg | 1281 x 1920 | 436px | 34.04% | 1301px | 67.76% | 23.06% |
9âž” | EF2000 rectangle | e77db44803b33494f10221413b3274d2.jpg | 1281 x 1920 | 513px | 40.05% | 1477px | 76.93% | 30.81% |
10âž” | 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.
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}
}
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 = { oct },
note = { visited on 2024-10-15 },
}
Download #
Please visit dataset homepage to download the data.
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