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Road Sign Detection Dataset

87742283
Tagenergy-and-utilities, self-driving
Taskobject detection
Release YearMade in 2020
LicenseCC0 1.0
Download221 MB

Summary #

Dataset LinkHomepage

Road Sign Detection is a dataset for an object detection task. Possible applications of the dataset could be in the utilities and automotive industries.

The dataset consists of 877 images with 1244 labeled objects belonging to 4 different classes including speedlimit, crosswalk, trafficlight, and other: stop.

Images in the Road Sign 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.

Dataset Poster

Explore #

Road Sign Detection dataset has 877 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 Road Sign DetectionSample image from Road Sign Detection
OpenSample annotation mask from Road Sign DetectionSample image from Road Sign Detection
OpenSample annotation mask from Road Sign DetectionSample image from Road Sign Detection
OpenSample annotation mask from Road Sign DetectionSample image from Road Sign Detection
OpenSample annotation mask from Road Sign DetectionSample image from Road Sign Detection
OpenSample annotation mask from Road Sign DetectionSample image from Road Sign Detection
OpenSample annotation mask from Road Sign DetectionSample image from Road Sign Detection
OpenSample annotation mask from Road Sign DetectionSample image from Road Sign Detection
OpenSample annotation mask from Road Sign DetectionSample image from Road Sign Detection
OpenSample annotation mask from Road Sign DetectionSample image from Road Sign Detection
OpenSample annotation mask from Road Sign DetectionSample image from Road Sign Detection
OpenSample annotation mask from Road Sign DetectionSample image from Road Sign Detection
OpenSample annotation mask from Road Sign DetectionSample image from Road Sign Detection
OpenSample annotation mask from Road Sign DetectionSample image from Road Sign Detection
OpenSample annotation mask from Road Sign DetectionSample image from Road Sign Detection
OpenSample annotation mask from Road Sign DetectionSample image from Road Sign Detection
OpenSample annotation mask from Road Sign DetectionSample image from Road Sign Detection
OpenSample annotation mask from Road Sign DetectionSample image from Road Sign Detection
OpenSample annotation mask from Road Sign DetectionSample image from Road Sign Detection
OpenSample annotation mask from Road Sign DetectionSample image from Road Sign Detection
OpenSample annotation mask from Road Sign DetectionSample image from Road Sign Detection
OpenSample annotation mask from Road Sign DetectionSample image from Road Sign Detection
OpenSample annotation mask from Road Sign DetectionSample image from Road Sign Detection
OpenSample annotation mask from Road Sign DetectionSample image from Road Sign Detection
OpenSample annotation mask from Road Sign DetectionSample image from Road Sign Detection
OpenSample annotation mask from Road Sign DetectionSample image from Road Sign Detection
OpenSample annotation mask from Road Sign DetectionSample image from Road Sign Detection
OpenSample annotation mask from Road Sign DetectionSample image from Road Sign Detection
OpenSample annotation mask from Road Sign DetectionSample image from Road Sign Detection
OpenSample annotation mask from Road Sign DetectionSample image from Road Sign Detection
👀
Have a look at 877 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 4 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 1-4 of 4
Class
Images
Objects
Count on image
average
Area on image
average
speedlimit
rectangle
678
783
1.15
3.68%
crosswalk
rectangle
170
200
1.18
5.3%
trafficlight
rectangle
104
170
1.63
7.79%
stop
rectangle
91
91
1
15.97%

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 1-4 of 4
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
speedlimit
rectangle
783
3.19%
68.79%
0.07%
9px
2.25%
332px
94%
52px
13.34%
9px
3%
329px
85%
crosswalk
rectangle
200
4.5%
52.52%
0.04%
7px
1.75%
300px
89.02%
56px
16.3%
6px
2%
236px
74.8%
trafficlight
rectangle
170
4.78%
56.3%
0.03%
8px
2%
383px
97.75%
84px
23.1%
5px
1.67%
176px
65.92%
stop
rectangle
91
15.97%
92.16%
0.08%
10px
2.5%
384px
96%
113px
33.57%
10px
3.33%
384px
96%

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 1244 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 1244
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
crosswalk
rectangle
road178.png
400 x 300
68px
17%
64px
21.33%
3.63%
2
speedlimit
rectangle
road351.png
400 x 300
79px
19.75%
81px
27%
5.33%
3
stop
rectangle
road185.png
400 x 300
18px
4.5%
12px
4%
0.18%
4
speedlimit
rectangle
road483.png
400 x 300
82px
20.5%
83px
27.67%
5.67%
5
speedlimit
rectangle
road191.png
400 x 300
37px
9.25%
32px
10.67%
0.99%
6
speedlimit
rectangle
road712.png
400 x 300
43px
10.75%
42px
14%
1.5%
7
speedlimit
rectangle
road712.png
400 x 300
42px
10.5%
42px
14%
1.47%
8
trafficlight
rectangle
road25.png
267 x 400
33px
12.36%
15px
3.75%
0.46%
9
trafficlight
rectangle
road25.png
267 x 400
28px
10.49%
12px
3%
0.31%
10
trafficlight
rectangle
road25.png
267 x 400
36px
13.48%
18px
4.5%
0.61%

License #

Road Sign Detection is under CC0 1.0 license.

Source

Citation #

If you make use of the Road Sign Detection data, please cite the following reference:

@dataset{Road Sign Detection,
	author={Andrew Maranhão},
	title={Road Sign Detection},
	year={2020},
	url={https://www.kaggle.com/datasets/andrewmvd/road-sign-detection}
}

Source

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

@misc{ visualization-tools-for-road-sign-detection-dataset,
  title = { Visualization Tools for Road Sign Detection Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/road-sign-detection } },
  url = { https://datasetninja.com/road-sign-detection },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { feb },
  note = { visited on 2024-02-24 },
}

Download #

Dataset Road Sign Detection 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='Road Sign Detection', 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.

. . .

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