Dataset Ninja LogoDataset Ninja:

DAC-SDC 2022 Dataset

9352012289
Tagsafety, drones
Taskobject detection
Release YearMade in 2022
LicenseMIT
Download7 GB

Introduction #

Xiaowei Xu, Xinyi Zhang, Bei Yuet al.

The authors created the DAC-SDC: Design Automation Conference System Design Contest 2022 Dataset to design and implement new algorithms based on object detection in images acquired from unmanned aerial vehicles (UAVs). The dataset includes 95 categories and 150 thousand images provided by a UAV company DJI.

Motivation

The 55th Design Automation Conference (DAC) introduced its inaugural System Design Contest (SDC) in 2018, featuring the Lower Power Object Detection Challenge (LPODC). This challenge tasked participants with devising and implementing innovative algorithms for object detection in images captured from unmanned aerial vehicles (UAVs). By providing a unified platform, the LPODC facilitated the development and comparison of state-of-the-art object detection algorithms while also fostering discussions on the insights gained from the entries.

The focus of the LPODC at DAC-SDC’18 was on applications involving unmanned aerial vehicles (UAVs), which typically entail stringent requirements for accuracy, real-time processing, and energy efficiency. Specifically, the LPODC entailed detecting a single object of interest, a critical task in UAV applications. Unlike conventional computer vision challenges like ImageNet and PASCAL VOC, which prioritize accuracy alone, the LPODC assessed overall performance based on a combination of throughput, power consumption, and detection accuracy. This comprehensive evaluation framework took into account the unique features of UAV applications, including real-time processing, energy constraints, and detection accuracy. Moreover, the dataset used in the LPODC comprised images captured from actual UAVs, thereby reflecting the real-world conditions and challenges encountered in UAV applications. Additionally, the LPODC offered participating teams the choice of two hardware platforms for their implementations: an embedded GPU (Jetson TX2 from Nvidia) and an FPGA SoC (PYNQ Z-1 board from Xilinx). These platforms, widely adopted for energy-efficient processing in UAVs, provided teams with flexibility in designing their solutions.

The publicly released dataset comprises a substantial number of manually annotated training images, while the testing dataset is reserved exclusively for evaluation purposes. In total, there are 150,000 images available provided by a UAV company DJI. Participating teams utilized the training dataset to train their models and algorithms. Subsequently, they submitted these trained models and algorithms to the organizers for evaluation. The evaluation process involved assessing various metrics such as throughput, energy efficiency, and detection accuracy. Evaluations were conducted at the conclusion of each month, with detailed rankings released thereafter. The final rankings were announced at the conclusion of the competition. The top three entries from both GPU and FPGA categories were honored, with their creators invited to present their work at a dedicated technical session during DAC.

The objective of the LPODC task is to conduct single-object detection in each image, with an axis-aligned bounding box delineating the object’s position and scale. Given the focus on UAV applications, certain key considerations come into play. Firstly, the object detection task entails pinpointing a specific object from the training dataset, rather than merely identifying objects belonging to a broader category. For instance, if images featuring person A are included in the training dataset, the goal is to detect person A specifically, rather than other individuals. Secondly, the object detection task necessitates achieving both high throughput and high accuracy, aligning with the stringent requirements of UAV applications. This combination of precision is crucial for effectively serving the needs of UAV operations.

Dataset description

The dataset sourced from DJI encompasses 12 categories of images and 95 sub-categories. Notably, in contrast to prevalent general-purpose datasets such as ImageNet and PASCAL VOC, the objects in this dataset are captured from a UAV perspective, showcasing various viewpoints.

Within the dataset, the majority of images feature objects sized between 1-2% of the captured image dimensions (640x360), which is characteristic of UAV-view imagery. This proportion maintains a favorable balance in terms of image brightness and information content. Most images exhibit moderate levels of brightness and information, akin to a Gaussian distribution, with fewer instances of excessively high or low brightness or information content.

Note: the authors did not provide a division of the dataset into training and testing.

ExpandExpand
Dataset LinkHomepageDataset LinkResearch PaperDataset LinkGitHubDataset LinkKaggle

Summary #

DAC-SDC: Design Automation Conference System Design Contest 2022 Dataset is a dataset for object detection and identification tasks. It is used in the search and rescue (SAR) industry.

The dataset consists of 93520 images with 93520 labeled objects belonging to 12 different classes including person, car, riding, and other: boat, group, wakeboard, drone, truck, paraglider, whale, building, and horseride.

Images in the DAC-SDC 2022 dataset have bounding box annotations. All images are labeled (i.e. with annotations). There is 1 split in the dataset: train (93520 images). Additionally, every image marked with its sequence tag. The dataset was released in 2022 by the University of Notre Dame, USA, The Chinese University, China, and University of Pittsburgh, USA.

Dataset Poster

Explore #

DAC-SDC 2022 dataset has 93520 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 DAC-SDC 2022Sample image from DAC-SDC 2022
OpenSample annotation mask from DAC-SDC 2022Sample image from DAC-SDC 2022
OpenSample annotation mask from DAC-SDC 2022Sample image from DAC-SDC 2022
OpenSample annotation mask from DAC-SDC 2022Sample image from DAC-SDC 2022
OpenSample annotation mask from DAC-SDC 2022Sample image from DAC-SDC 2022
OpenSample annotation mask from DAC-SDC 2022Sample image from DAC-SDC 2022
OpenSample annotation mask from DAC-SDC 2022Sample image from DAC-SDC 2022
OpenSample annotation mask from DAC-SDC 2022Sample image from DAC-SDC 2022
OpenSample annotation mask from DAC-SDC 2022Sample image from DAC-SDC 2022
OpenSample annotation mask from DAC-SDC 2022Sample image from DAC-SDC 2022
OpenSample annotation mask from DAC-SDC 2022Sample image from DAC-SDC 2022
OpenSample annotation mask from DAC-SDC 2022Sample image from DAC-SDC 2022
OpenSample annotation mask from DAC-SDC 2022Sample image from DAC-SDC 2022
OpenSample annotation mask from DAC-SDC 2022Sample image from DAC-SDC 2022
OpenSample annotation mask from DAC-SDC 2022Sample image from DAC-SDC 2022
OpenSample annotation mask from DAC-SDC 2022Sample image from DAC-SDC 2022
OpenSample annotation mask from DAC-SDC 2022Sample image from DAC-SDC 2022
OpenSample annotation mask from DAC-SDC 2022Sample image from DAC-SDC 2022
OpenSample annotation mask from DAC-SDC 2022Sample image from DAC-SDC 2022
OpenSample annotation mask from DAC-SDC 2022Sample image from DAC-SDC 2022
OpenSample annotation mask from DAC-SDC 2022Sample image from DAC-SDC 2022
OpenSample annotation mask from DAC-SDC 2022Sample image from DAC-SDC 2022
OpenSample annotation mask from DAC-SDC 2022Sample image from DAC-SDC 2022
OpenSample annotation mask from DAC-SDC 2022Sample image from DAC-SDC 2022
OpenSample annotation mask from DAC-SDC 2022Sample image from DAC-SDC 2022
OpenSample annotation mask from DAC-SDC 2022Sample image from DAC-SDC 2022
OpenSample annotation mask from DAC-SDC 2022Sample image from DAC-SDC 2022
OpenSample annotation mask from DAC-SDC 2022Sample image from DAC-SDC 2022
OpenSample annotation mask from DAC-SDC 2022Sample image from DAC-SDC 2022
OpenSample annotation mask from DAC-SDC 2022Sample image from DAC-SDC 2022
πŸ‘€
Have a look at 93520 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
personβž”
rectangle
27965
27965
1
1.09%
carβž”
rectangle
25014
25014
1
0.9%
ridingβž”
rectangle
16999
16999
1
1.37%
boatβž”
rectangle
5207
5207
1
3.43%
groupβž”
rectangle
4815
4815
1
0.15%
wakeboardβž”
rectangle
3300
3300
1
0.22%
droneβž”
rectangle
2576
2576
1
0.52%
truckβž”
rectangle
2378
2378
1
1.13%
paragliderβž”
rectangle
1694
1694
1
2.77%
whaleβž”
rectangle
1533
1533
1
3.89%

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 12
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
person
rectangle
27965
1.09%
7.88%
0.07%
10px
2.78%
227px
63.06%
73px
20.28%
10px
1.56%
84px
13.12%
car
rectangle
25014
0.9%
7.48%
0.03%
5px
1.39%
133px
36.94%
43px
11.82%
7px
1.09%
146px
22.81%
riding
rectangle
16999
1.37%
12.65%
0.12%
16px
4.44%
192px
53.33%
66px
18.25%
12px
1.88%
155px
24.22%
boat
rectangle
5207
3.43%
31.85%
0.05%
8px
2.22%
239px
66.39%
66px
18.47%
14px
2.19%
307px
47.97%
group
rectangle
4815
0.15%
0.37%
0.02%
11px
3.06%
45px
12.5%
33px
9.2%
2px
0.31%
23px
3.59%
wakeboard
rectangle
3300
0.22%
1.23%
0.01%
6px
1.67%
66px
18.33%
25px
6.83%
4px
0.62%
51px
7.97%
drone
rectangle
2576
0.52%
2.23%
0.19%
17px
4.72%
62px
17.22%
30px
8.31%
20px
3.12%
83px
12.97%
truck
rectangle
2378
1.13%
2.76%
0.01%
4px
1.11%
91px
25.28%
48px
13.28%
5px
0.78%
94px
14.69%
paraglider
rectangle
1694
2.77%
10.2%
0.55%
51px
14.17%
169px
46.94%
91px
25.33%
25px
3.91%
149px
23.28%
whale
rectangle
1533
3.89%
12.95%
0.65%
52px
14.44%
162px
45%
95px
26.45%
29px
4.53%
190px
29.69%

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 93520 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 93520
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”
riding
rectangle
riding13_1271.jpg
360 x 640
77px
21.39%
33px
5.16%
1.1%
2βž”
person
rectangle
person20_1119.jpg
360 x 640
75px
20.83%
31px
4.84%
1.01%
3βž”
riding
rectangle
riding4_0321.jpg
360 x 640
29px
8.06%
24px
3.75%
0.3%
4βž”
car
rectangle
car22_720 (6)_0239.jpg
360 x 640
47px
13.06%
45px
7.03%
0.92%
5βž”
car
rectangle
car10_1869.jpg
360 x 640
70px
19.44%
56px
8.75%
1.7%
6βž”
person
rectangle
person18_000709.jpg
360 x 640
171px
47.5%
53px
8.28%
3.93%
7βž”
car
rectangle
car17_720 (1)_0035.jpg
360 x 640
57px
15.83%
56px
8.75%
1.39%
8βž”
car
rectangle
car22_720 (6)_0494.jpg
360 x 640
41px
11.39%
39px
6.09%
0.69%
9βž”
person
rectangle
person17_001155.jpg
360 x 640
38px
10.56%
12px
1.88%
0.2%
10βž”
riding
rectangle
riding14_7283.jpg
360 x 640
104px
28.89%
54px
8.44%
2.44%

License #

DAC-SDC: Design Automation Conference System Design Contest 2022 Dataset is under MIT license.

Source

Citation #

If you make use of the DAC-SDC data, please cite the following reference:

@dataset{DAC-SDC,
  author={Xiaowei Xu and Xinyi Zhang and Bei Yu and Xiaobo Sharon Hu and Christopher Rowen and Jingtong Hu and Yiyu Shi},
  title={DAC-SDC: Design Automation Conference System Design Contest 2022 Dataset},
  year={2022},
  url={https://byuccl.github.io/dac_sdc_2022/}
}

Source

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

@misc{ visualization-tools-for-dac-sdc-dataset,
  title = { Visualization Tools for DAC-SDC 2022 Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/dac-sdc-2022 } },
  url = { https://datasetninja.com/dac-sdc-2022 },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { apr },
  note = { visited on 2024-04-14 },
}

Download #

Dataset DAC-SDC 2022 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='DAC-SDC 2022', 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.

. . .

Disclaimer #

Our gal from the legal dep told us we need to post this:

Dataset Ninja provides visualizations and statistics for some datasets that can be found online and can be downloaded by general audience. Dataset Ninja is not a dataset hosting platform and can only be used for informational purposes. The platform does not claim any rights for the original content, including images, videos, annotations and descriptions. Joint publishing is prohibited.

You take full responsibility when you use datasets presented at Dataset Ninja, as well as other information, including visualizations and statistics we provide. You are in charge of compliance with any dataset license and all other permissions. You are required to navigate datasets homepage and make sure that you can use it. In case of any questions, get in touch with us at hello@datasetninja.com.