A Rapid Segmentation Method of Highway Surface Point Cloud Data Based on a Supervoxel and Improved Region Growing Algorithm

• Published in: Applied sciences 2024-04, Vol.14 (7), p.2852
• Main Authors: Zhao, Wenshuo, Ning, Yipeng, Jia, Xiang, Chai, Dashuai, Su, Fei, Wang, Shengli
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Analysis; Automation; Buildings; Classification; Deep learning; Economic development; Efficiency; Freeways; Lasers; Maintenance and repair; Methods; mobile laser scanning; Neural networks; point cloud; region growing; Remodeling, restoration, etc; Repair & maintenance; road surface; Roads; Roads & highways; Semantics; supervoxel; Surveys
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app14072852

Mobile laser scanning (MLS) systems have become an important technology for collecting and measuring road information for highway maintenance and reconstruction services. However, the efficient and accurate extraction of unstructured road surfaces from MLS point cloud data collected on highways is challenging. Specifically, the complex and unstructured characteristics of road surveying point cloud data lead to traditional 3D point cloud segmentation. When traditional 3D point cloud algorithms extract unstructured road surfaces, over-segmentation and under-segmentation often occur, which affects efficiency and accuracy. To solve these problems, this study introduces an enhanced road extraction method that integrates supervoxel and trajectory information into a traditional region growing algorithm. The method involves two main steps: first, a supervoxel data structure is applied to reconstruct the original MLS point cloud data, which diminishes the calculation time of the point cloud feature vector and accelerates the merging speed of a similar region; second, the trajectory information of the vehicle is used to optimize the seed selection strategy of the regio growing algorithm, which improves the accuracy of road surface extraction. Finally, two typical highway section tests (flat road and slope road) were conducted to validate the positioning performance of the proposed algorithm in an MLS point cloud. The results show that, compared with three kinds of traditional road surface segmentation algorithms, our method achieves an average extraction recall and precision of 99.1% and 96.0%, and by calculating the recall and precision, an F1 score of 97.5% can be obtained to evaluate the performance of the proposed method, for both datasets. Additionally, our method exhibits an average road surface extraction time that is 45.0%, 50.3%, and 55.8% faster than those of the other three automated segmentation algorithms.