ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-2-2021
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2021, 145–151, 2021
https://doi.org/10.5194/isprs-annals-V-2-2021-145-2021
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2021, 145–151, 2021
https://doi.org/10.5194/isprs-annals-V-2-2021-145-2021

  17 Jun 2021

17 Jun 2021

SEGMENTATION OF TRAFFIC SIGNS FROM POLES WITH MATHEMATICAL MORPHOLOGY APPLIED TO POINT CLOUDS

J. Balado1,2, M. Soilán3, L. Díaz-Vilariño1,2, and P. van Oosterom2 J. Balado et al.
  • 1Universidade de Vigo, CINTECX, GeoTECH Group. 36310 Vigo, Spain
  • 2Delft University of Technology, Faculty of Architecture and the Built Environment. 2628 BL Delft, The Netherlands
  • 3University of Salamanca, Department of Cartographic and Terrain Engineering, 05003 Ávila, Spain

Keywords: Mobile Laser Scanning, topographic LiDAR, traffic signs, morphological opening, mathematical morphology, image processing

Abstract. Traffic signs are one of the most relevant road assets for driving, as the safety of drivers depends to a great extent on their correct location. In this paper two methods are compared for the segmentation of the sign and the pole supporting it. Both methods are based on the morphological opening to identify the sign points, the first one directly employs the mathematical morphology directly applied to point clouds and the second one through point cloud rasterization into images. The comparison was conducted on twenty real traffic signs acquired with Mobile Laser Scanning obtaining point clouds from environments with signposts, traffic lights and lampposts. The results showed a correct segmentation of the signs, obtaining a F-score of 0.81 by the point-based method and a 0.75 by 2D image method. In particular, the point-based mathematical morphology proved to be more accurate in the segmentation of traffic sings installed on traffic lights and lampposts, avoiding over detection shown by the 2D image method.