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The Mapillary Traffic Sign Dataset for Detection and Classification on a Global Scale

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12368))

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Abstract

Traffic signs are essential map features for smart cities and navigation. To develop accurate and robust algorithms for traffic sign detection and classification, a large-scale and diverse benchmark dataset is required. In this paper, we introduce a new traffic sign dataset of 105K street-level images around the world covering 400 manually annotated traffic sign classes in diverse scenes, wide range of geographical locations, and varying weather and lighting conditions. The dataset includes 52K fully annotated images. Additionally, we show how to augment the dataset with 53K semi-supervised, partially annotated images. This is the largest and the most diverse traffic sign dataset consisting of images from all over the world with fine-grained annotations of traffic sign classes. We run extensive experiments to establish strong baselines for both detection and classification tasks. In addition, we verify that the diversity of this dataset enables effective transfer learning for existing large-scale benchmark datasets on traffic sign detection and classification. The dataset is freely available for academic research (www.mapillary.com/dataset/trafficsign) .

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Notes

  1. 1.

    www.mapillary.com/app is a street-level imagery platform hosting images collected by members of their community.

  2. 2.

    Details on how scene properties are defined and derived are included in the supplementary materials.

  3. 3.

    We convert the segmentations of traffic-sign–front instances to bounding boxes by taking the minimum and maximum in the x, y axes. Note that this conversion can be inaccurate if signs are occluded.

  4. 4.

    We convert their results to the format used by MTSD and evaluate using our metrics.

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Authors and Affiliations

  1. Facebook, Menlo Park, USA

    Christian Ertler, Jerneja Mislej, Tobias Ollmann, Lorenzo Porzi, Gerhard Neuhold & Yubin Kuang

Authors
  1. Christian Ertler
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  2. Jerneja Mislej
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  3. Tobias Ollmann
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  4. Lorenzo Porzi
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  5. Gerhard Neuhold
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  6. Yubin Kuang
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Corresponding author

Correspondence to Christian Ertler .

Editor information

Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Ertler, C., Mislej, J., Ollmann, T., Porzi, L., Neuhold, G., Kuang, Y. (2020). The Mapillary Traffic Sign Dataset for Detection and Classification on a Global Scale. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12368. Springer, Cham. https://doi.org/10.1007/978-3-030-58592-1_5

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  • DOI: https://doi.org/10.1007/978-3-030-58592-1_5

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  • Online ISBN: 978-3-030-58592-1

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