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NTUT 4K Drone Photo Dataset

4095121923
Tagdrones, aerial
Taskobject detection
Release YearMade in 2022
Licenseunknown

Introduction #

Kuan-Ting (K. T.) Lai

In the NTUT 4K Drone Photo Dataset for Human Detection authors furnish 4K photos extracted from drone videos captured in Taiwan. Authors claim, that contemporary drones are outfitted with 4K video cameras, and the heightened resolution of the images facilitates modern object detectors in discerning smaller objects. Despite this capability, many drone image datasets typically offer only downscaled images. The dataset is curated by the AIoT Lab at the National Taiwan University of Technology (NTUT).

Sometimes humans may be blocked. In this case, the following convention is proposed:

  • block25 (Object is blocked 25~50%)
  • block50 (Object is blocked 50~75%)
  • block75 (Object is blocked 75~90%)

Also, the dataset propose recognizable objects for the purpose of identification task.

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Summary #

NTUT 4K Drone Photo Dataset for Human Detection is a dataset for object detection and identification tasks. Possible applications of the dataset could be in the drone inspection domain.

The dataset consists of 4095 images with 50553 labeled objects belonging to 12 different classes including walk, recognizable, stand, and other: riding, sit, block50, block25, push, block75, watchphone, baseball, and soccer.

Images in the NTUT 4K Drone Photo dataset have bounding box annotations. There are 26 (1% of the total) unlabeled images (i.e. without annotations). There are 2 splits in the dataset: train (2156 images) and test (1939 images). Additionally, every recognizable class has its own id tag. The dataset was released in 2022 by the Taipei Tech AIoT Lab, Taiwan.

Dataset Poster

Explore #

NTUT 4K Drone Photo dataset has 4095 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 NTUT 4K Drone PhotoSample image from NTUT 4K Drone Photo
OpenSample annotation mask from NTUT 4K Drone PhotoSample image from NTUT 4K Drone Photo
OpenSample annotation mask from NTUT 4K Drone PhotoSample image from NTUT 4K Drone Photo
OpenSample annotation mask from NTUT 4K Drone PhotoSample image from NTUT 4K Drone Photo
OpenSample annotation mask from NTUT 4K Drone PhotoSample image from NTUT 4K Drone Photo
OpenSample annotation mask from NTUT 4K Drone PhotoSample image from NTUT 4K Drone Photo
OpenSample annotation mask from NTUT 4K Drone PhotoSample image from NTUT 4K Drone Photo
OpenSample annotation mask from NTUT 4K Drone PhotoSample image from NTUT 4K Drone Photo
OpenSample annotation mask from NTUT 4K Drone PhotoSample image from NTUT 4K Drone Photo
OpenSample annotation mask from NTUT 4K Drone PhotoSample image from NTUT 4K Drone Photo
OpenSample annotation mask from NTUT 4K Drone PhotoSample image from NTUT 4K Drone Photo
OpenSample annotation mask from NTUT 4K Drone PhotoSample image from NTUT 4K Drone Photo
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Have a look at 4095 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 12 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 12
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
walkβž”
rectangle
2486
9783
3.94
0.34%
recognizableβž”
rectangle
1912
32388
16.94
2.32%
standβž”
rectangle
1291
3801
2.94
0.27%
ridingβž”
rectangle
681
2384
3.5
0.61%
sitβž”
rectangle
428
905
2.11
0.17%
block50βž”
rectangle
277
323
1.17
0.1%
block25βž”
rectangle
241
282
1.17
0.14%
pushβž”
rectangle
176
177
1.01
0.13%
block75βž”
rectangle
173
225
1.3
0.12%
watchphoneβž”
rectangle
147
167
1.14
0.12%

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 1-10 of 11
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
recognizable
rectangle
32388
0.15%
25.88%
0.01%
20px
0.93%
2160px
100%
145px
6.72%
12px
0.31%
996px
25.94%
walk
rectangle
9783
0.09%
1.99%
0.01%
22px
1.02%
571px
26.44%
100px
4.64%
17px
0.44%
309px
8.05%
stand
rectangle
3801
0.09%
3.83%
0.01%
28px
1.3%
569px
26.34%
93px
4.3%
22px
0.57%
649px
16.9%
riding
rectangle
2384
0.18%
0.39%
0.01%
19px
0.88%
230px
10.65%
140px
6.48%
33px
0.86%
230px
5.99%
sit
rectangle
905
0.08%
1.88%
0.02%
32px
1.48%
373px
17.27%
76px
3.53%
30px
0.78%
418px
10.89%
block50
rectangle
323
0.08%
1.14%
0.01%
22px
1.02%
404px
18.7%
86px
3.99%
25px
0.65%
234px
6.09%
block25
rectangle
282
0.12%
3.46%
0.01%
39px
1.81%
569px
26.34%
103px
4.78%
23px
0.6%
530px
13.8%
block75
rectangle
225
0.09%
0.43%
0.01%
19px
0.88%
197px
9.12%
87px
4.01%
20px
0.52%
246px
6.41%
push
rectangle
177
0.12%
0.3%
0.03%
61px
2.82%
143px
6.62%
109px
5.02%
39px
1.02%
173px
4.51%
watchphone
rectangle
167
0.11%
0.22%
0.05%
46px
2.13%
158px
7.31%
92px
4.26%
59px
1.54%
136px
3.54%

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 50553 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 50553
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”
recognizable
rectangle
Drone_049.mp4_t-26.166667.jpg
2160 x 3840
188px
8.7%
104px
2.71%
0.24%
2βž”
recognizable
rectangle
Drone_049.mp4_t-26.166667.jpg
2160 x 3840
168px
7.78%
58px
1.51%
0.12%
3βž”
recognizable
rectangle
Drone_049.mp4_t-26.166667.jpg
2160 x 3840
163px
7.55%
59px
1.54%
0.12%
4βž”
recognizable
rectangle
Drone_049.mp4_t-26.166667.jpg
2160 x 3840
156px
7.22%
70px
1.82%
0.13%
5βž”
recognizable
rectangle
Drone_049.mp4_t-26.166667.jpg
2160 x 3840
116px
5.37%
54px
1.41%
0.08%
6βž”
recognizable
rectangle
Drone_049.mp4_t-26.166667.jpg
2160 x 3840
169px
7.82%
100px
2.6%
0.2%
7βž”
recognizable
rectangle
Drone_049.mp4_t-26.166667.jpg
2160 x 3840
139px
6.44%
30px
0.78%
0.05%
8βž”
recognizable
rectangle
Drone_049.mp4_t-26.166667.jpg
2160 x 3840
88px
4.07%
52px
1.35%
0.06%
9βž”
recognizable
rectangle
Drone_049.mp4_t-26.166667.jpg
2160 x 3840
58px
2.69%
22px
0.57%
0.02%
10βž”
recognizable
rectangle
Drone_049.mp4_t-26.166667.jpg
2160 x 3840
144px
6.67%
62px
1.61%
0.11%

License #

License is unknown for the NTUT 4K Drone Photo Dataset for Human Detection dataset.

Source

Citation #

If you make use of the NTUT 4K Drone Photo data, please cite the following reference:

@dataset{NTUT 4K Drone Photo,
  author={Kuan-Ting (K. T.) Lai},
  title={NTUT 4K Drone Photo Dataset for Human Detection},
  year={2022},
  url={https://www.kaggle.com/datasets/kuantinglai/ntut-4k-drone-photo-dataset-for-human-detection/data}
}

Source

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

@misc{ visualization-tools-for-ntut-4k-drone-photo-dataset,
  title = { Visualization Tools for NTUT 4K Drone Photo Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/ntut-4k-drone-photo } },
  url = { https://datasetninja.com/ntut-4k-drone-photo },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jul },
  note = { visited on 2024-07-25 },
}

Download #

Please visit dataset homepage to download the data.

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