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Jun 20, 2021 2:26 PM


We present the 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. We hope that this dataset allows for easy testing of objection detection approaches, especially for small objects in larger images. The scenes cover a decent variety of road scenes and typical difficulties:

Data description

This dataset contains 13427 camera images at a resolution of 1280x720 pixels and contains about 24000 annotated traffic lights. The annotations include bounding boxes of traffic lights as well as the current state (active light) of each traffic light. The camera images are provided as raw 12bit HDR images taken with a red-clear-clear-blue filter and as reconstructed 8-bit RGB color images. The RGB images are provided for debugging and can also be used for training. However, the RGB conversion process has some drawbacks. Some of the converted images may contain artifacts and the color distribution may seem unusual.

Dataset specifications:

Training set:

  • 5093 images

  • Annotated about every 2 seconds

  • 10756 annotated traffic lights

  • Median traffic lights width: ~8.6 pixels

  • 15 different labels

  • 170 lights are partially occluded

Test set:

  • 8334 consecutive images

  • Annotated at about 15 fps

  • 13486 annotated traffic lights

  • Median traffic light width: 8.5 pixels

  • 4 labels (red, yellow, green, off)

  • 2088 lights are partially occluded

For the test set, every frame is annotated and temporal information was used to improve the label accuracy. The test-set was recorded independently from the training set, but within the same region. The data-set was created to prototype traffic light detection approaches, it is not intended to cover all cases and not to be used for production.


Please use the following citation when referencing the dataset:

  title={A Deep Learning Approach to Traffic Lights: Detection, Tracking, and Classification},
  author={Behrendt, Karsten and Novak, Libor},
  booktitle={Robotics and Automation (ICRA), 2017 IEEE International Conference on},
🎉Many thanks to Graviti Open Datasets for contributing the dataset
Basic Information
Application ScenariosNot Available
AnnotationsNot Available
TasksNot Available
Updated on2021-01-20 03:20:00
Data TypeNot Available
Data Volume13.43K
Annotation Amount0
File Size0B
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