Segmentation and classification of road markings using MLS data

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• 作者：Mario Soilá ; n^a ; ^{msoilan@uvigo.es} ; Belé ; n Riveiro^b ; Joaquí ; n Martí ; nez-Sá ; nchez^a ; Pedro Arias^a
• 关键词：Mobile laser scanning ; Point cloud segmentation ; Road marking ; Classification ; Smart cities
• 刊名：ISPRS Journal of Photogrammetry and Remote Sensing
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：123
• 期：Complete
• 页码：94-103
• 全文大小：2002 K
• 卷排序：123

文摘

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.