RT Journal Article
T1 Segmentation and classification of road markings using MLS data
A1 Soilán Rodríguez, Mario
A1 Riveiro Rodríguez, Belén
A1 Martínez Sánchez, Joaquín
A1 Arias Sánchez, Pedro
K1 3305.06 Ingeniería Civil
AB Traffic signs are one of the most important safety elements in a road network. Particularly, road markings provide information about the limits and direction of each road lane, or warn the drivers about potential danger. The optimal condition of road markings contributes to a better road safety. Mobile Laser Scanning technology can be used for infrastructure inspection and specifically for traffic sign detection and inventory. This paper presents a methodology for the detection and semantic characterization of the most common road markings, namely pedestrian crossings and arrows. The 3D point cloud data acquired by a LYNX Mobile Mapper system is filtered in order to isolate reflective points in the road, and each single element is hierarchically classified using Neural Networks. State of the art results are obtained for the extraction and classification of the markings, with F-scores of 94% and 96% respectively. Finally, data from classified markings are exported to a GIS layer and maintenance criteria based on the aforementioned data are proposed.
PB ISPRS Journal of Photogrammetry and Remote Sensing
SN 09242716
YR 2017
FD 2017-01
LK http://hdl.handle.net/11093/1336
UL http://hdl.handle.net/11093/1336
LA eng
NO ISPRS Journal of Photogrammetry and Remote Sensing, 123, 94-103 (2017)
NO Ministerio de Economía y Competitividad | Ref. BES-2014-067736
DS Investigo
RD 26-sep-2023