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BSTLD Dataset

134274263
Tagenergy-and-utilities, self-driving
Taskobject detection
Release YearMade in 2017
Licensecustom
Download18 GB

Introduction #

Karsten Behrendt, Libor Novak, Rami Botros

The authors present the BSTLD: Bosch Small Traffic Lights Dataset, an accurate dataset for vision-based traffic light detection. Vision-only based traffic light detection and tracking is a vital step on the way to fully automated driving in urban environments. The authors hope that this dataset allows for easy testing of objection detection approaches, especially for small objects in larger images.

Note, similar BSTLD: Bosch Small Traffic Lights Dataset datasets are also available on the DatasetNinja.com:

Motivation

Accurate detection and classification of traffic lights are vital for the success of automated driving in urban settings. Presently, there is a lack of systems capable of reliably perceiving traffic lights in real-time, without relying on map-based information, and from adequate distances necessary for seamless urban driving. While automated driving on highways has garnered considerable research attention, urban environments present a fresh set of challenges demanding more sophisticated algorithms across various domains, including perception, behavioral planning, and collision avoidance systems.

Within the realm of perception, identifying and categorizing traffic signs and lights stands out as a critical task. Traffic lights, in particular, pose a formidable challenge due to their small size and potential confusion with other urban elements such as lamps, decorations, and reflections. Recent advancements in deep neural networks have significantly enhanced various fields of machine learning, particularly computer vision. Deep learning techniques have been successfully employed for tasks such as image classification, end-to-end object detection, pixel-precise object segmentation, and numerous other applications.

image

Sample detections of small traffic lights in an image. The top image is taken at the full resolution of 1280 × 720. At the bottom, the enlarged crop shows detected traffic lights of size about 6 × 12 pixels. All lights are correctly classified as yellow.

Dataset description

The authors publish the Bosch Small Traffic Lights Dataset, an accurately labeled dataset for detecting, classifying, and tracking traffic lights. Dataset contains RGB color images at the resolution of 1280 × 720 pixels. For training, the authors collected more than 5000 images, mainly along El Camino Real in the San Francisco Bay Area in California. Overall, 10,756 traffic lights are labeled within those images. The different traffic light sizes within the training set vary between approximately 1 and 85 pixels in width with the mean of 11.3 pixels.

Width Height Area
Minimum 1.12 0.25 0.28
Average 11.18 24.32 404.52
Median 8.55 18.93 158.8
Maximum 98.0 207.0 20286.0

Training set bounding box sizes in pixels.

The label distribution of the different traffic light states is heavily skewed towards the three most common types which are green, red, and yellow. Also, because of the difference between the sampling frequency of our camera and the traffic light refresh rate, a lot of traffic lights frequently appear to be off. Whenever this is the case, the authors opted for labeling them as off instead of their current state.

Training In % Test In %
Red 3057 28.42 5321 39.44
Red Straight 9 0.08 0 0
Red Straight Left 1 0.01 0 0
Red Left 1092 10.15 0 0
Red Right 5 0.05 0 0
Yellow 444 4.13 154 0.01
Green 5207 48.41 7569 56.10
Green Straight 20 0.19 0 0
Green Straight Left 1 0.01 0 0
Green Straight Right 3 0.03 0 0
Green Left 178 1.65 0 0
Green Right 13 0.12 0 0
Off 726 6.75 449 0.03

Label distribution over the training and test set.

For testing, the authors collected a stereo video sequence with odometry along University Avenue in Palo Alto, California. In contrast to the training images, those images are taken and labeled consecutively at a frequency of 15.6 frames per second. Since the bounding boxes are labeled consecutively, it is possible to evaluate a tracker on them. In total, the test set comprises 8,334 labeled image files, encompassing 13,493 traffic lights. Notably, 2,094 of these lights are labeled despite being obscured by various objects. Remarkably, our test set exclusively includes labels for traffic lights in off, green, red, and yellow states. Table III offers a comprehensive breakdown of the diverse sizes of traffic lights within our test set. This dataset is intended to serve as a rigorous benchmark for the detection of traffic lights, or more broadly, small objects within images. Additionally, the authors present our findings as a baseline using this dataset.

Width Height Area
Minimum 1.875 3.250 11.718
Average 9.430 26.745 313.349
Median 8.500 24.500 212.109
Maximum 48.375 104.500 4734.000

Test set traffic light bounding box sizes in pixels.

Identifying objects within images presents significant challenges, particularly when aiming for consistent performance in real-time across a spectrum of object sizes. In the case of traffic lights, achieving real-time detection with minimal false negatives and false positives is imperative. A high false negative rate can result in missed traffic lights, an outcome deemed unacceptable for autonomous vehicles. Conversely, false positive detections can induce erratic behavior in automated vehicles, such as halting for non-existent red traffic lights.

Proposed neural network

The YOLO architecture has demonstrated impressive performance on datasets like PASCAL VOC 2007 and 2012, achieving processing speeds of up to 45 frames per second. However, a grid-based approach to detection presents limitations, particularly in suggesting bounding boxes per cell. Authors have noted difficulties in detecting small objects and clusters of objects. Additionally, there’s a challenge in balancing image input sizes, processing speed, and memory usage. Given that the average width of traffic lights in our datasets is only 10 pixels, adjustments are necessary to enable the network to detect small objects effectively. To address this, the authors have implemented a multi-step approach. Instead of feeding the entire image into the network, they partition the image into different patches and evaluate each patch separately. During training, random crops of the image are utilized as inputs to the network, leading to improved convergence speed and accuracy. For the final detection phase, they focus on three specific crops in the upper part of the image, as this is where most traffic lights are typically located. The authors have also developed a heatmap to visualize the occurrences of traffic lights. Each network has a receptive field of 448 × 448 pixels, allowing for comprehensive analysis of the image data.

image

Sample image from test-set. The authors network is evaluated at the positions within the image, shown in red, teal, and yellow. Two green traffic lights are detected, one by a cell of the yellow network, the other by red.

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Dataset LinkHomepageDataset LinkResearch PaperDataset LinkGitHub

Summary #

BSTLD: Bosch Small Traffic Lights Dataset is a dataset for an object detection task. It is used in the automotive industry.

The dataset consists of 13427 images with 24241 labeled objects belonging to 4 different classes including green, red, off, and other: yellow.

Images in the BSTLD dataset have bounding box annotations. There are 3127 (23% of the total) unlabeled images (i.e. without annotations). There are 2 splits in the dataset: test (8334 images) and train (5093 images). Alternatively, the dataset could be split into 6 sequences: 2015-10-05-14-40-46_bag (1546 images), 2015-10-05-11-26-32_bag (651 images), 2015-10-05-10-55-33_bag (540 images), 2017-02-03-11-44-56_los_altos_mountain_view_traffic_lights_bag (518 images), 2015-05-29-15-29-39_arastradero_traffic_light_loop_bag (347 images), and 2015-10-05-10-52-01_bag (68 images), or into 5 traffic light direction: left (1270 objects), straight (29 objects), right (18 objects), straight right (3 objects), and straight left (2 objects). Additionally, labels contains information about occluded. Explore it in supervisely labeling tool. The dataset was released in 2017 by the Bosch Automated Driving Team, Czech.

Dataset Poster

Explore #

BSTLD dataset has 13427 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 BSTLDSample image from BSTLD
OpenSample annotation mask from BSTLDSample image from BSTLD
OpenSample annotation mask from BSTLDSample image from BSTLD
OpenSample annotation mask from BSTLDSample image from BSTLD
OpenSample annotation mask from BSTLDSample image from BSTLD
OpenSample annotation mask from BSTLDSample image from BSTLD
OpenSample annotation mask from BSTLDSample image from BSTLD
OpenSample annotation mask from BSTLDSample image from BSTLD
OpenSample annotation mask from BSTLDSample image from BSTLD
OpenSample annotation mask from BSTLDSample image from BSTLD
OpenSample annotation mask from BSTLDSample image from BSTLD
OpenSample annotation mask from BSTLDSample image from BSTLD
OpenSample annotation mask from BSTLDSample image from BSTLD
OpenSample annotation mask from BSTLDSample image from BSTLD
OpenSample annotation mask from BSTLDSample image from BSTLD
OpenSample annotation mask from BSTLDSample image from BSTLD
OpenSample annotation mask from BSTLDSample image from BSTLD
OpenSample annotation mask from BSTLDSample image from BSTLD
OpenSample annotation mask from BSTLDSample image from BSTLD
OpenSample annotation mask from BSTLDSample image from BSTLD
OpenSample annotation mask from BSTLDSample image from BSTLD
OpenSample annotation mask from BSTLDSample image from BSTLD
OpenSample annotation mask from BSTLDSample image from BSTLD
OpenSample annotation mask from BSTLDSample image from BSTLD
OpenSample annotation mask from BSTLDSample image from BSTLD
OpenSample annotation mask from BSTLDSample image from BSTLD
OpenSample annotation mask from BSTLDSample image from BSTLD
OpenSample annotation mask from BSTLDSample image from BSTLD
OpenSample annotation mask from BSTLDSample image from BSTLD
OpenSample annotation mask from BSTLDSample image from BSTLD
👀
Have a look at 13427 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
green
rectangle
6617
12990
1.96
0.09%
red
rectangle
5213
9485
1.82
0.07%
off
rectangle
930
1168
1.26
0.04%
yellow
rectangle
394
598
1.52
0.06%

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
green
rectangle
12990
0.05%
2.23%
0%
4px
0.56%
208px
28.89%
28px
3.9%
3px
0.23%
99px
7.73%
red
rectangle
9485
0.04%
1.02%
0%
4px
0.56%
154px
21.39%
25px
3.53%
3px
0.23%
95px
7.42%
off
rectangle
1168
0.04%
0.66%
0%
5px
0.69%
108px
15%
22px
3.03%
3px
0.23%
65px
5.08%
yellow
rectangle
598
0.04%
0.7%
0%
5px
0.69%
119px
16.53%
23px
3.22%
3px
0.23%
54px
4.22%

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 24241 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 24241
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
green
rectangle
689700.png
720 x 1280
14px
1.94%
8px
0.62%
0.01%
2
green
rectangle
689700.png
720 x 1280
4px
0.56%
7px
0.55%
0%
3
green
rectangle
689700.png
720 x 1280
5px
0.69%
5px
0.39%
0%
4
green
rectangle
524786.png
720 x 1280
103px
14.31%
37px
2.89%
0.41%
5
green
rectangle
718362.png
720 x 1280
50px
6.94%
23px
1.8%
0.12%
6
green
rectangle
718362.png
720 x 1280
44px
6.11%
21px
1.64%
0.1%
7
red
rectangle
718362.png
720 x 1280
6px
0.83%
5px
0.39%
0%
8
green
rectangle
718362.png
720 x 1280
6px
0.83%
5px
0.39%
0%
9
green
rectangle
718362.png
720 x 1280
50px
6.94%
14px
1.09%
0.08%
10
red
rectangle
583650.png
720 x 1280
33px
4.58%
16px
1.25%
0.06%

License #

ADD CUSTOM LICENSE MANUALLY

Source

Citation #

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

@dataset{BSTLD,
  author={Karsten Behrendt and Libor Novak and Rami Botros},
  title={BSTLD: Bosch Small Traffic Lights Dataset},
  year={2017},
  url={https://hci.iwr.uni-heidelberg.de/content/bosch-small-traffic-lights-dataset}
}

Source

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

@misc{ visualization-tools-for-bstld-dataset,
  title = { Visualization Tools for BSTLD Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/bstld } },
  url = { https://datasetninja.com/bstld },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { mar },
  note = { visited on 2024-03-05 },
}

Download #

Dataset BSTLD 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='BSTLD', 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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