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A Dataset for Lane Instance Segmentation in Urban Environments

  • Brook Roberts
  • Sebastian KaltwangEmail author
  • Sina Samangooei
  • Mark Pender-Bare
  • Konstantinos Tertikas
  • John Redford
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11212)

Abstract

Autonomous vehicles require knowledge of the surrounding road layout, which can be predicted by state-of-the-art CNNs. This work addresses the current lack of data for determining lane instances, which are needed for various driving manoeuvres. The main issue is the time-consuming manual labelling process, typically applied per image. We notice that driving the car is itself a form of annotation. Therefore, we propose a semi-automated method that allows for efficient labelling of image sequences by utilising an estimated road plane in 3D based on where the car has driven and projecting labels from this plane into all images of the sequence. The average labelling time per image is reduced to 5 s and only an inexpensive dash-cam is required for data capture. We are releasing a dataset of 24,000 images and additionally show experimental semantic segmentation and instance segmentation results.

Keywords

Dataset Urban driving Road Lane Instance segmentation Semi-automated annotation Partial labels 

Notes

Acknowledgements

We would like to thank our colleagues Tom Westmacott, Joel Jakubovic and Robert Chandler, who have contributed to the implementation of the annotation software.

Supplementary material

474213_1_En_33_MOESM1_ESM.mp4 (7.5 mb)
Supplementary material 1 (mp4 7653 KB)

Supplementary material 2 (mp4 21079 KB)

474213_1_En_33_MOESM3_ESM.mp4 (2.5 mb)
Supplementary material 3 (mp4 2516 KB)

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Brook Roberts
    • 1
  • Sebastian Kaltwang
    • 1
    Email author
  • Sina Samangooei
    • 1
  • Mark Pender-Bare
    • 1
  • Konstantinos Tertikas
    • 1
  • John Redford
    • 1
  1. 1.FiveAI Ltd.CambridgeUK

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