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Digital Breakthrough 2022: Car Object Dataset

48211234
Tagsafety
Taskobject detection
Release YearMade in 2022
Licenseunknown

Introduction #

Andrew Teplov

The Digital Breakthrough 2022: Ulyanovsk - Car Object Detection dataset comprises 482 labeled images specifically focusing on car object detection. Part of the Ulyanovsk region (Russia) AI competition, this dataset challenges participants to develop a real-time distance determination algorithm for cars in different photos. The intended use is for navigation systems to warn about unsafe distances, promoting road safety protocols.

About Digital Breakthrough: Season AI (Ulyanovsk Region)

According to the press service of the Moscow Traffic Police, by the end of 2021, non-compliance with the distance between cars has become the most dangerous violation. It is because of non-compliance with the safe distance to the car in front that people most often die and get into accidents on the roads.

Maintaining a safe distance from the vehicle in front is one of the important criteria for road safety. In real-time, such a parameter cannot be monitored with the help of surveillance cameras, and it is also impossible to estimate what distance the driver observes while driving around the city. To control safe driving, a solution is needed that will be able to track the distance between cars in real-time. This will reduce the number of accidents and save the lives of drivers and passengers.

The participants of the championship are faced with the task of developing an algorithm that allows determining the distance to the car in front in real-time, using a dataset of photos of cars from different distances. Subsequently, this algorithm can be used in navigation systems to warn of a dangerous approach and to monitor compliance with the distance.

Author’s solution

The author’s solution is based on two datasets:

  • Digital Breakthrough 2022: Ulyanovsk - Car Plate Detection (available on DatasetNinja)
  • Digital Breakthrough 2022: Ulyanovsk - Car Object Detection (current)

The baseline provided by the organizers of the championship was taken as the basis of the solution, which was improved, namely, in addition to detecting the car, the number, and the car-mating badge were also detected, which gave a greater increase in speed. Additional models were retrained on the competition data train (manual marking using an online service makesense.ai ) using the YOLOv5 (YOLOv5m6) model. CatBoostRegressor with improved parameters was used to predict the distance.

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Dataset LinkHomepageDataset LinkCompetition PageDataset LinkGitHub

Summary #

Digital Breakthrough 2022: Ulyanovsk - Car Object Detection is a dataset for an object detection task. Possible applications of the dataset could be in the safety industry.

The dataset consists of 482 images with 482 labeled objects belonging to 1 single class (car).

Images in the Digital Breakthrough 2022: Car Object dataset have bounding box annotations. All images are labeled (i.e. with annotations). There is 1 split in the dataset: train (482 images). The dataset was released in 2022 by the Government of the Ulyanovsk Region and NordClan.

Dataset Poster

Explore #

Digital Breakthrough 2022: Car Object dataset has 482 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 Digital Breakthrough 2022: Car ObjectSample image from Digital Breakthrough 2022: Car Object
OpenSample annotation mask from Digital Breakthrough 2022: Car ObjectSample image from Digital Breakthrough 2022: Car Object
OpenSample annotation mask from Digital Breakthrough 2022: Car ObjectSample image from Digital Breakthrough 2022: Car Object
OpenSample annotation mask from Digital Breakthrough 2022: Car ObjectSample image from Digital Breakthrough 2022: Car Object
OpenSample annotation mask from Digital Breakthrough 2022: Car ObjectSample image from Digital Breakthrough 2022: Car Object
OpenSample annotation mask from Digital Breakthrough 2022: Car ObjectSample image from Digital Breakthrough 2022: Car Object
OpenSample annotation mask from Digital Breakthrough 2022: Car ObjectSample image from Digital Breakthrough 2022: Car Object
OpenSample annotation mask from Digital Breakthrough 2022: Car ObjectSample image from Digital Breakthrough 2022: Car Object
OpenSample annotation mask from Digital Breakthrough 2022: Car ObjectSample image from Digital Breakthrough 2022: Car Object
OpenSample annotation mask from Digital Breakthrough 2022: Car ObjectSample image from Digital Breakthrough 2022: Car Object
OpenSample annotation mask from Digital Breakthrough 2022: Car ObjectSample image from Digital Breakthrough 2022: Car Object
OpenSample annotation mask from Digital Breakthrough 2022: Car ObjectSample image from Digital Breakthrough 2022: Car Object
👀
Have a look at 482 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 1 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 1-1 of 1
Class
Images
Objects
Count on image
average
Area on image
average
car
rectangle
482
482
1
5.01%

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 1-1 of 1
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
car
rectangle
482
5.01%
46.79%
0.59%
250px
8.27%
2139px
70.73%
641px
21.18%
288px
7.14%
2667px
66.15%

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 482 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 482
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
car
rectangle
img_2765.jpg
3024 x 4032
358px
11.84%
419px
10.39%
1.23%
2
car
rectangle
img_1959.jpg
3024 x 4032
528px
17.46%
665px
16.49%
2.88%
3
car
rectangle
img_1912.jpg
3024 x 4032
653px
21.59%
744px
18.45%
3.98%
4
car
rectangle
img_1863.jpg
3024 x 4032
491px
16.24%
635px
15.75%
2.56%
5
car
rectangle
img_2747.jpg
3024 x 4032
434px
14.35%
508px
12.6%
1.81%
6
car
rectangle
img_2866.jpg
3024 x 4032
633px
20.93%
824px
20.44%
4.28%
7
car
rectangle
img_2289.jpg
3024 x 4032
293px
9.69%
327px
8.11%
0.79%
8
car
rectangle
img_2858.jpg
3024 x 4032
2097px
69.35%
2587px
64.16%
44.49%
9
car
rectangle
img_2586.jpg
3024 x 4032
471px
15.58%
565px
14.01%
2.18%
10
car
rectangle
img_2742.jpg
3024 x 4032
344px
11.38%
420px
10.42%
1.18%

License #

License is unknown for the Digital Breakthrough 2022: Ulyanovsk - Car Object Detection dataset.

Source

Citation #

If you make use of the Digital Breakthrough 2022: Car Object data, please cite the following reference:

@dataset{Digital Breakthrough 2022: Car Object,
  author={Andrew Teplov},
  title={Digital Breakthrough 2022: Ulyanovsk - Car Object Detection},
  year={2022},
  url={https://www.kaggle.com/datasets/andrewteplov/car-plate-object-detetcion}
}

Source

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

@misc{ visualization-tools-for-digital-breakthrough-2022-ulyanovsk-car-object-dataset,
  title = { Visualization Tools for Digital Breakthrough 2022: Car Object Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/digital-breakthrough-2022-ulyanovsk-car-object } },
  url = { https://datasetninja.com/digital-breakthrough-2022-ulyanovsk-car-object },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { mar },
  note = { visited on 2024-03-05 },
}

Download #

Please visit dataset homepage to download the data.

. . .

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