Road Sign Detection - Dataset Ninja

Summary #

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.

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.

Sample annotation mask from Road Sign Detection

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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.

Rows 1-4 of 4

Class ㅤ	Images ㅤ	Objects ㅤ	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.

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.

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.

Rows 1-10 of 1244

Object ID ㅤ	Class ㅤ	Image name click row to open	Image size height x width	Height ㅤ	Height ㅤ	Width ㅤ	Width ㅤ	Area ㅤ
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 = { may },
  note = { visited on 2024-05-16 },
}

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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Disclaimer #

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

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