Dataset Ninja LogoDataset Ninja:

S2TLD Dataset

57865147
Tagenergy-and-utilities, self-driving
Taskobject detection
Release YearMade in 2022
LicenseMIT
Download1 GB

Introduction #

Xue Yang, Junchi Yan, Xiaokang Yanget al.

The S2TLD: Small Traffic Light Dataset is the traffic light dataset. The scenes cover a variety of lighting, weather and traffic conditions, including busy street scenes inner-city, dense stop-and-go traffic, strong changes in illumination/exposure, flickering/fluctuating traffic lights, multiple visible traffic lights, images parts that can be confused with traffic lights.

Note, similar S2TLD: Small Traffic Light Dataset datasets are also available on the DatasetNinja.com:

Motivation

Reliable traffic light detection and classification is crucial for automated driving in urban environments. Currently, there are no systems that can reliably perceive traffic lights in real-time, without map-based information, and in sufficient distances needed for smooth urban driving.

Dataset description

The S2TLD contains 5,786 images of approximately 1, 080 Γ— 1, 920 pixels (1,222 images) and 720 Γ— 1, 280 pixels (4,564 images). It also contains 5 categories (namely red, yellow, green, off and wait on) of 14,130 instances. The scenes cover a variety of lighting, weather and traffic conditions, including busy street scenes inner city, dense stop-and-go traffic, strong changes in illumination/exposure, flickering/fluctuating traffic lights, multiple visible traffic lights, image parts that can be confused with traffic lights (e.g. large round tail lights).

image

Illustrations of the five categories and different lighting and weather conditions in collected S2TLD dataset.

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Dataset LinkHomepageDataset LinkResearch Paper

Summary #

S2TLD: SJTU Small Traffic Light Dataset is a dataset for an object detection task. It is used in the automotive industry.

The dataset consists of 5786 images with 14130 labeled objects belonging to 5 different classes including red, green, off, and other: wait on and yellow.

Images in the S2TLD dataset have bounding box annotations. There are 3 (0% of the total) unlabeled images (i.e. without annotations). There are no pre-defined train/val/test splits in the dataset. Alternatively, the dataset could be split into 2 normal: normal 2 (3785 images) and normal 1 (779 images), or into 2 resolution: 720x1280 (4564 images) and 1080x1920 (1222 images). The dataset was released in 2022 by the Shanghai Jiao Tong University, China and Anhui University, China.

Dataset Poster

Explore #

S2TLD dataset has 5786 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 S2TLDSample image from S2TLD
OpenSample annotation mask from S2TLDSample image from S2TLD
OpenSample annotation mask from S2TLDSample image from S2TLD
OpenSample annotation mask from S2TLDSample image from S2TLD
OpenSample annotation mask from S2TLDSample image from S2TLD
OpenSample annotation mask from S2TLDSample image from S2TLD
OpenSample annotation mask from S2TLDSample image from S2TLD
OpenSample annotation mask from S2TLDSample image from S2TLD
OpenSample annotation mask from S2TLDSample image from S2TLD
OpenSample annotation mask from S2TLDSample image from S2TLD
OpenSample annotation mask from S2TLDSample image from S2TLD
OpenSample annotation mask from S2TLDSample image from S2TLD
OpenSample annotation mask from S2TLDSample image from S2TLD
OpenSample annotation mask from S2TLDSample image from S2TLD
OpenSample annotation mask from S2TLDSample image from S2TLD
OpenSample annotation mask from S2TLDSample image from S2TLD
OpenSample annotation mask from S2TLDSample image from S2TLD
OpenSample annotation mask from S2TLDSample image from S2TLD
OpenSample annotation mask from S2TLDSample image from S2TLD
OpenSample annotation mask from S2TLDSample image from S2TLD
OpenSample annotation mask from S2TLDSample image from S2TLD
OpenSample annotation mask from S2TLDSample image from S2TLD
OpenSample annotation mask from S2TLDSample image from S2TLD
OpenSample annotation mask from S2TLDSample image from S2TLD
OpenSample annotation mask from S2TLDSample image from S2TLD
OpenSample annotation mask from S2TLDSample image from S2TLD
OpenSample annotation mask from S2TLDSample image from S2TLD
OpenSample annotation mask from S2TLDSample image from S2TLD
OpenSample annotation mask from S2TLDSample image from S2TLD
OpenSample annotation mask from S2TLDSample image from S2TLD
πŸ‘€
Have a look at 5786 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 5 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 1-5 of 5
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
redβž”
rectangle
3777
7708
2.04
0.26%
greenβž”
rectangle
2931
5344
1.82
0.28%
offβž”
rectangle
328
507
1.55
0.18%
wait onβž”
rectangle
305
306
1
0.15%
yellowβž”
rectangle
176
265
1.51
0.22%

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 1-5 of 5
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
red
rectangle
7708
0.13%
2.16%
0%
1px
0.09%
220px
30.56%
48px
6.29%
6px
0.36%
209px
10.89%
green
rectangle
5344
0.15%
3.5%
0.01%
7px
0.83%
273px
37.92%
51px
6.78%
6px
0.47%
177px
9.22%
off
rectangle
507
0.12%
1.54%
0.01%
14px
1.48%
185px
25.69%
46px
6.39%
6px
0.47%
115px
6.25%
wait on
rectangle
306
0.15%
0.78%
0.02%
9px
0.83%
56px
5.19%
22px
2.06%
36px
1.88%
321px
16.72%
yellow
rectangle
265
0.15%
1.26%
0.01%
11px
1.02%
171px
22.64%
50px
6.42%
8px
0.62%
142px
7.4%

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 14130 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 14130
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”
red
rectangle
002471.jpg
720 x 1280
68px
9.44%
26px
2.03%
0.19%
2βž”
red
rectangle
002471.jpg
720 x 1280
69px
9.58%
26px
2.03%
0.19%
3βž”
red
rectangle
002471.jpg
720 x 1280
59px
8.19%
24px
1.88%
0.15%
4βž”
red
rectangle
002471.jpg
720 x 1280
59px
8.19%
23px
1.8%
0.15%
5βž”
red
rectangle
003633.jpg
720 x 1280
66px
9.17%
26px
2.03%
0.19%
6βž”
green
rectangle
003633.jpg
720 x 1280
68px
9.44%
26px
2.03%
0.19%
7βž”
green
rectangle
003633.jpg
720 x 1280
51px
7.08%
18px
1.41%
0.1%
8βž”
red
rectangle
003633.jpg
720 x 1280
47px
6.53%
19px
1.48%
0.1%
9βž”
wait on
rectangle
2020-04-04 11_13_00.777872252.jpg
1080 x 1920
36px
3.33%
321px
16.72%
0.56%
10βž”
green
rectangle
2020-04-04 11_13_00.777872252.jpg
1080 x 1920
66px
6.11%
168px
8.75%
0.53%

License #

S2TLD: SJTU Small Traffic Light Dataset is under MIT license.

Source

Citation #

If you make use of the S2TLD data, please cite the following reference:

@article{yang2022scrdet++,
  title={Scrdet++: Detecting small, cluttered and rotated objects via instance-level feature denoising and rotation loss smoothing},
  author={Yang, Xue and Yan, Junchi and Liao, Wenlong and Yang, Xiaokang and Tang, Jin and He, Tao},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2022},
  publisher={IEEE}
}

Source

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

@misc{ visualization-tools-for-s2tld-dataset,
  title = { Visualization Tools for S2TLD Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/s2tld } },
  url = { https://datasetninja.com/s2tld },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { feb },
  note = { visited on 2024-02-24 },
}

Download #

Dataset S2TLD 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='S2TLD', 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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