Segmentation and classification of road markings using MLS data

Abstract

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.

Automatic Road Markings Extraction, Classification and Vectorization From Mobile Laser Scanning Data

“…(Kumar et al, 2014) made use of binary morphological operations and prior knowledges to complete and refine the road markings detected by thresholding. (Soilan et al, 2017) used a Gaussian Mixture Model (GMM) to roughly separate road marking points from other road surface points.Then an adaptive thresholding based on Otsu method is applied on the intensity image to extract road marking pixels robustly. (Wen et al, 2019) adopted a modified U-net to accurately extract road markings from a regularized intensity image.…”

“…Classification: (Yu et al, 2017) utilized trajectory and road arrangement to classify large markings and applied Deep Boltzmann Machines to classify small size irregular markings. (Soilan et al, 2017) achieve the classification by adopting a two-layer feedforward neural network taking road marking images' geometry based feature or Histogram of Oriented Gradients as input. As for arrow markings, the classification is accomplished through the correlation calculation between target and model arrow images.…”

“…Complement and Modeling: (Yang et al, 2012) used the Hough Transform to fit the boundary lines and dashed center lines. According to (Soilan et al, 2017), a GIS layer for road inventory could be created and updated efficiently. However, the inventory is based on images, thus having limited accuracy.…”

Automatic extraction of road features in urban environments using dense ALS data

“…For this, an algorithm by Wang et al (2015) was modified. Instead of using the distance between the normal vector of each point and a dominant normal vector to compute a saliency map, the following modifications were made:  Normal vectors were obtained within a voxel space using a cubic cell grid (Soilán et al, 2017). For each voxel, a single point was defined as the centroid of the points in the cell, thereby creating a de facto downsampling of the data.…”

Combined Lane Mapping Using a Mobile Mapping System

“…However, due to the inconsistency of the reflective intensity, a single global threshold may result in incomplete extraction or false positives. Some studies [40][41][42][43][44] used multithreshold methods to reduce the impact of this inconsistency. Chen et al [40] selected the peaks of intensity values as candidate lane-marking points for each scan line through an adaptive thresholding process.…”