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Road surface detection and differentiation considering surface damages

Road Surface Semantic Segmentation

The semantic segmentation GT for road surfaces contains 701 frames from RTK dataset. Classes are defined as follows:

  • Background, everything being unrelated to the road surface;
  • Asphalt, roads with asphalt surface;
  • Paved, different pavements (eg.: Cobblestone);
  • Unpaved, for unpaved roads;
  • Markings, to the road markings;
  • Speed-Bump, for the speed-bumps on the road;
  • Cats-Eye, for the cats-eye found on the road, both on the side and in the center of the path;
  • Storm-Drain, usually at the side edges of the road;
  • Patch, for the various patches found on asphalt road;
  • Water-Puddle, we use this class also for muddy regions;
  • Pothole, for different types and sizes of potholes, no matter if they are on asphalt, paved or unpaved roads;
  • Cracks, used in different road damages, like ruptures.

Citation:

@misc{rateke:2020.3,
   title = {Road surface detection and differentiation considering surface damages},
   author = {Thiago Rateke and Aldo von Wangenheim},
   journal={Autonomous Robots},
   year={2021},
   month={Jan},
   day={11},
   issn={1573-7527},
   doi={10.1007/s10514-020-09964-3},
   url={https://doi.org/10.1007/s10514-020-09964-3}
}