Iterative Tensor Voting for Pavement Crack Extraction Using Mobile Laser Scanning Data

作者：Guan, Haiyan; Li, Jonathan^*; Yu, Yongtao; Chapman, Michael; Wang, Hanyun; Wang, Cheng; Zhai, Ruifang

来源：IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(3): 1527-1537.

DOI：10.1109/TGRS.2014.2344714

• Georeferenced
• intensity
• iterative tensor voting (ITV)
• ITVCrack
• mobile laser scanning (MLS)
• pavement crack extraction

The assessment of pavement cracks is one of the essential tasks for road maintenance. This paper presents a novel framework, called ITVCrack, for automated crack extraction based on iterative tensor voting (ITV), from high-density point clouds collected by a mobile laser scanning system. The proposed ITVCrack comprises the following: 1) the preprocessing involving the separation of road points from nonroad points using vehicle trajectory data; 2) the generation of the georeferenced feature (GRF) image from the road points; and 3) the ITV-based crack extraction from the noisy GRF image, followed by an accurate delineation of the curvilinear cracks. Qualitatively, the method is applicable for pavement cracks with low contrast, low signal-to-noise ratio, and bad continuity. Besides the application to GRF images, the proposed framework demonstrates much better crack extraction performance when quantitatively compared to existing methods on synthetic data and pavement images.

• 出版日期2015-3
• 单位中国人民解放军国防科学技术大学; 华中农业大学; 厦门大学