Tropical Geography ›› 2020, Vol. 40 ›› Issue (5): 893-902.doi: 10.13284/j.cnki.rddl.003263

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Semantic Classification of Pole-Like Traffic Facilities in Complex Road Scenes Based on LiDAR Point Cloud

Yong Tang1(), Zheng Xiang2, Tengping Jiang3   

  1. 1.Speed Space-Time Information Technology Co. , Ltd. , Nanjing 210000, China
    2.Nanjing Guotu Information Industry Co. , Ltd. , Nanjing 210000, China
    3.Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
  • Received:2020-03-08 Revised:2020-04-19 Online:2020-09-28 Published:2020-10-10


Pole-like objects are commonly occurring features on roads, and their identification in photographs is essential to the management and mapping of road information. In particular, mobile laser scanning systems comprise one of the most accurate and efficient techniques to gather road-related geospatial information. The automatic detection and classification of pole-like objects based on the point cloud data gathered by such systems enable significant reduction in costs and improvement in efficiency of mapping functions. This paper proposes a complete, fully automated processing framework that detects and classifies pole-like objects appearing in images of highways and urban streets based on the associated point cloud data. The primary workflow of the framework includes three steps: data preprocessing, detection, and classification of pole-like objects. During data preprocessing, an advanced filtration algorithm is used to automatically separate ground points from non-ground points. Subsequently, the non-ground points are clustered and overlapping clusters are further separated via a collaborative iterative graph cutting algorithm. During object detection, all available information, including shape information and position guidance, are integrated to detect pole-like objects. During object classification, the multiple aggregation levels of features and the contextual features corresponding to each object are calculated and transmitted into a random forest classifier to classify the detected pole-like structures. The proposed method was tested on three road scene datasets. The overall MCC coefficient corresponding to all three datasets was observed to be 95.6% during detection, and the overall classification accuracy corresponding to the three datasets was 96.1% during classification. Further, comparative experiments with respect to existing techniques were conducted, and the results demonstrated that the proposed method significantly improves the recognition of road facilities.

Key words: point cloud processing, object recognition, semantic segmentation, multiple aggregation level features, complex road scenes, pole-like road facilities

CLC Number: 

  • U491