Abstract
Mobile LiDAR is an emerging advanced technology for capturing three-dimensional road information at a large scale effectively and precisely. Pole-like road facilities are crucial street infrastructures as they provide valuable information for road mapping and road inventory. Thus, the automated localization and classification of road facilities are necessary. This paper proposes a voxel-based method to detect and classify pole-like objects in an expressway environment based on the spatially independent and vertical height continuity analysis. First, the ground points are eliminated, and the nonground points are merged into clusters. Second, the pole-like objects are extracted using horizontal cross section analysis and minimum vertical height criteria. Finally, a set of knowledge-based rules, which comprise height features and geometric shape, is constructed to classify the detected road poles into different types of road facilities. Two test sites of point clouds in an expressway environment, which are located in Bangkok, Thailand, are used to assess the proposed method. The proposed method extracts the pole-like road facilities from two datasets with a detection rate of 95.1% and 93.5% and an overall quality of 89.7% and 98.0% in the classification stage, respectively. This shows that the algorithm could be a promising alternative for the localization and classification of pole-like road facilities with acceptable accuracy.
Highlights
Expressway facilities, such as lighting poles, traffic signs, speed limit posts, overhead signs, emergency telephone posts, or telecommunication stations, are crucial components of the transportation infrastructure and play a vital role in expressway asset management
A shortage of lighting poles, speed limit posts, or the inaccuracy of a traffic sign can lead to severe traffic casualties [2]. erefore, collecting and updating all expressway facility information are essential for an asset management agency
Considering the drawbacks of past studies, this paper proposes the voxel-based method to detect PLOs based on spatially independent and vertical height continuity analysis
Summary
Us, the automated localization and classification of road facilities are necessary. Is paper proposes a voxelbased method to detect and classify pole-like objects in an expressway environment based on the spatially independent and vertical height continuity analysis. The pole-like objects are extracted using horizontal cross section analysis and minimum vertical height criteria. A set of knowledge-based rules, which comprise height features and geometric shape, is constructed to classify the detected road poles into different types of road facilities. E proposed method extracts the pole-like road facilities from two datasets with a detection rate of 95.1% and 93.5% and an overall quality of 89.7% and 98.0% in the classification stage, respectively. Is shows that the algorithm could be a promising alternative for the localization and classification of pole-like road facilities with acceptable accuracy
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