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RTSD: Russian Traffic Sign Images Dataset

13450106129
Tagself-driving
Taskobject detection
Release YearMade in 2019
Licenseunknown

Introduction #

Tingir Badmaev, Vlad Shakhuro, Anton Konushin

This RTSD: Russian Traffic Sign Images Dataset was created for training and testing the algorithms of traffic sign recognition. Frames are obtained from widescreen digital video recorder which captures 5 frames per second. Frame resolution is from 1280×720 to 1920×1080. Frames are captured in different seasons (spring, autumn, winter), time of day (morning, afternoon, evening) and in different weather conditions (rain, snow, bright sun).

Motivation

Recently, the development of control systems for self-driving cars has been progressing rapidly. A critical component of these systems is the algorithm for recognizing road signs. Beyond autonomous vehicles, sign recognition is also used in driver assistance systems and to automate road maintenance services. Modern methods of object recognition in images rely on deep learning models. To create a high-quality detection model, a well-labeled dataset is essential. However, the process of annotating datasets is both expensive and time-consuming, requiring meticulous manual work and verification to account for human errors.

Synthetic data offers a solution by simplifying data collection. It can be generated quickly, at no cost, without annotation errors, and in virtually unlimited quantities, significantly reducing the cost of obtaining data. The task of recognizing road signs inherently involves highly unbalanced classes. Many classes of signs are underrepresented in training samples, complicating the model training process. Previous research has demonstrated that using realistic synthetic datasets with augmentations of rare classes of road images can enhance traffic sign recognition models.

Dataset description

The authors explore the recognition of both common and rare road signs, emphasizing that the importance of rare signs is equal to that of common ones. Firstly, they enhance the current markup of the Russian traffic signs dataset in a semi-automatic mode, adding 9,000 new signs: 4,000 to the test set and 5,000 to the training set. Secondly, they conduct an experimental evaluation of modern methods for the classification and detection of road signs. Instead of using a combined classification method, the authors employ a single neural network trained on a mixture of real and synthetic data. To boost the performance of road sign detection and classification, they utilize stochastic weight averaging. This approach results in a significant improvement in recognition metrics for both rare and common signs.

This dataset is designed for training and testing traffic sign recognition algorithms. The frames are captured using a widescreen digital video recorder at a rate of 5 frames per second, with resolutions ranging from 1280×720 to 1920×1080. The frames are taken in various seasons (spring, autumn, winter), at different times of the day (morning, afternoon, evening), and under diverse weather conditions (rain, snow, bright sun). This dataset exceeds other public traffic sign datasets in terms of the number of frames, sign classes, physical signs, and images of signs. The sign labeling process is conducted in two stages. In the first stage, tracks of physical objects are identified in sequential frames. In the second stage, indistinguishable signs are discarded, and each physical sign is assigned a class.

image

The problem of recognizing road signs is unsolved for a large number of classes of signs, since there are many rare classes of signs. For systems that recognize road signs, the ability to quickly adapt in the event of a new type of road signs is important. With the emergence of a new class of signs, it is difficult to collect a sufficient number of real training examples. In the presence of synthetically generated images of this sign, the ability to classify a new type of sign can be quickly introduced into the system. High-quality synthetic samples allow us to solve the problem with missing classes and data collection with examples of these signs. The quality of the detectors can also be improved with synthetic signs. In this task, the consistency of the appearance of the sign with the background is especially important. The correct positioning of the new synthetic road sign is also very important.

Real image Image with additional signs

Note: Сlasses of the specified Russian road signs comply with GOST requirements.

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Dataset LinkHomepageDataset LinkResearch Paper

Summary #

RTSD: Russian Traffic Sign Images Dataset is a dataset for an object detection task. It is used in the automotive industry.

The dataset consists of 13450 images with 19805 labeled objects belonging to 106 different classes including 5_19_1, 2_1, 5_16, and other: 3_27, 4_1_1, 1_23, 3_20, 3_24_n40, 5_15_3, 5_15_2, 5_20, 5_15_1, 5_15_2_2, 5_15_5, 4_2_3, 7_3, 1_17, 3_1, 4_2_1, 2_3_2, 1_25, 6_4, 4_1_4, 1_22, 5_5, 6_6, 3_24_n20, 2_3_3, and 78 more.

Images in the RTSD: Russian Traffic Sign Images Dataset dataset have bounding box annotations. There are 2844 (21% of the total) unlabeled images (i.e. without annotations). There are 2 splits in the dataset: train (10367 images) and test (3083 images). Alternatively, the dataset could be split into 6 sign types: blue rect (8949 instances), prohibitory (2822 instances), danger (2466 instances), main road (2138 instances), mandatory (1721 instances), and blue border (1709 instances). The dataset was released in 2019 by the Lomonosov Moscow State University, Russia and NRU Higher School of Economics, Russia.

Dataset Poster

Explore #

RTSD: Russian Traffic Sign Images Dataset dataset has 13450 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 RTSD: Russian Traffic Sign Images DatasetSample image from RTSD: Russian Traffic Sign Images Dataset
OpenSample annotation mask from RTSD: Russian Traffic Sign Images DatasetSample image from RTSD: Russian Traffic Sign Images Dataset
OpenSample annotation mask from RTSD: Russian Traffic Sign Images DatasetSample image from RTSD: Russian Traffic Sign Images Dataset
OpenSample annotation mask from RTSD: Russian Traffic Sign Images DatasetSample image from RTSD: Russian Traffic Sign Images Dataset
OpenSample annotation mask from RTSD: Russian Traffic Sign Images DatasetSample image from RTSD: Russian Traffic Sign Images Dataset
OpenSample annotation mask from RTSD: Russian Traffic Sign Images DatasetSample image from RTSD: Russian Traffic Sign Images Dataset
OpenSample annotation mask from RTSD: Russian Traffic Sign Images DatasetSample image from RTSD: Russian Traffic Sign Images Dataset
OpenSample annotation mask from RTSD: Russian Traffic Sign Images DatasetSample image from RTSD: Russian Traffic Sign Images Dataset
OpenSample annotation mask from RTSD: Russian Traffic Sign Images DatasetSample image from RTSD: Russian Traffic Sign Images Dataset
OpenSample annotation mask from RTSD: Russian Traffic Sign Images DatasetSample image from RTSD: Russian Traffic Sign Images Dataset
OpenSample annotation mask from RTSD: Russian Traffic Sign Images DatasetSample image from RTSD: Russian Traffic Sign Images Dataset
OpenSample annotation mask from RTSD: Russian Traffic Sign Images DatasetSample image from RTSD: Russian Traffic Sign Images Dataset
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Have a look at 13450 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 106 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 106
Class
Images
Objects
Count on image
average
Area on image
average
5_19_1
rectangle
3035
5201
1.71
0.17%
2_1
rectangle
2004
2023
1.01
0.15%
5_16
rectangle
1168
1236
1.06
0.09%
3_27
rectangle
741
773
1.04
0.09%
4_1_1
rectangle
498
527
1.06
0.09%
1_23
rectangle
496
510
1.03
0.13%
3_20
rectangle
412
417
1.01
0.07%
3_24_n40
rectangle
388
410
1.06
0.11%
5_15_3
rectangle
365
373
1.02
0.1%
5_15_2
rectangle
355
1135
3.2
0.2%

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 106
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
5_19_1
rectangle
5201
0.1%
2.5%
0.01%
17px
1.57%
219px
21.94%
32.11px
3.88%
17px
0.89%
221px
11.51%
2_1
rectangle
2023
0.15%
3.21%
0.01%
17px
1.57%
182px
23.06%
35.46px
4.44%
17px
0.89%
205px
13.91%
5_16
rectangle
1236
0.08%
0.82%
0.02%
18px
1.85%
124px
17.22%
32.81px
4.26%
17px
0.89%
88px
6.17%
5_15_2
rectangle
1135
0.06%
0.52%
0.03%
17px
2.36%
69px
9.58%
22.86px
3.17%
17px
1.33%
70px
5.47%
3_27
rectangle
773
0.09%
1.47%
0.02%
17px
1.67%
110px
15.28%
27.73px
3.74%
17px
1.04%
123px
9.61%
4_1_1
rectangle
527
0.09%
0.96%
0.01%
17px
1.57%
113px
13.06%
27.07px
3.62%
17px
0.89%
112px
7.34%
1_23
rectangle
510
0.13%
2.37%
0.02%
17px
1.67%
165px
18.19%
33.35px
4.08%
17px
0.99%
183px
13.05%
3_20
rectangle
417
0.07%
1.15%
0.02%
17px
1.85%
114px
13.89%
23.4px
3.21%
17px
1.04%
118px
8.28%
3_24_n40
rectangle
410
0.11%
1.55%
0.01%
17px
1.57%
129px
16.81%
30.91px
3.75%
17px
0.89%
140px
9.77%
5_15_3
rectangle
373
0.1%
1.63%
0.02%
17px
1.85%
107px
14.86%
27.17px
3.74%
17px
0.94%
146px
11.41%

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 19805 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 19805
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
2_1
rectangle
autosave10_10_2012_12_52_21_0.jpg
720 x 1280
32px
4.44%
31px
2.42%
0.11%
2
1_2
rectangle
autosave09_10_2012_11_29_51_2.jpg
720 x 1280
20px
2.78%
21px
1.64%
0.05%
3
3_24_n40
rectangle
autosave09_10_2012_11_29_51_2.jpg
720 x 1280
19px
2.64%
19px
1.48%
0.04%
4
1_2
rectangle
autosave09_10_2012_11_29_51_2.jpg
720 x 1280
24px
3.33%
24px
1.88%
0.06%
5
3_1
rectangle
autosave01_02_2012_12_59_37.jpg
720 x 1280
32px
4.44%
32px
2.5%
0.11%
6
4_1_1
rectangle
autosave16_10_2012_11_12_38_0.jpg
720 x 1280
31px
4.31%
30px
2.34%
0.1%
7
4_1_1
rectangle
autosave16_10_2012_11_12_38_0.jpg
720 x 1280
17px
2.36%
19px
1.48%
0.04%
8
2_1
rectangle
autosave16_10_2012_11_12_38_0.jpg
720 x 1280
42px
5.83%
41px
3.2%
0.19%
9
2_5
rectangle
autosave01_02_2012_11_11_32.jpg
720 x 1280
22px
3.06%
22px
1.72%
0.05%
10
5_19_1
rectangle
autosave09_10_2012_12_50_32_2.jpg
720 x 1280
36px
5%
17px
1.33%
0.07%

License #

License is unknown for the RTSD: Russian Traffic Sign Images Dataset dataset.

Source

Citation #

If you make use of the RTSD: Russian Traffic Sign Images Dataset data, please cite the following reference:

@dataset{RTSD: Russian Traffic Sign Images Dataset,
  author={Tingir Badmaev and Vlad Shakhuro and Anton Konushin},
  title={RTSD: Russian Traffic Sign Images Dataset},
  year={2019},
  url={https://graphics.cs.msu.ru/projects/traffic-sign-recognition.html}
}

Source

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

@misc{ visualization-tools-for-russian-traffic-sign-dataset,
  title = { Visualization Tools for RTSD: Russian Traffic Sign Images Dataset Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/russian-traffic-sign } },
  url = { https://datasetninja.com/russian-traffic-sign },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jul },
  note = { visited on 2024-07-25 },
}

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Please visit dataset homepage to download the data.

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