A Collaborative Monitoring Method for Traffic Situations under Urban Road Emergencies

Xiang, Min; An, Yulin

doi:10.3390/app13031311

Cited by 2 publications

(1 citation statement)

References 44 publications

(72 reference statements)

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“…Mobile LiDAR systems can rapidly obtain three-dimensional spatial information for static objects in road scenes. They play a crucial role in the spatial analysis of typical features in road scenes, such as traffic sign occlusion [1], the optimization of monitoring areas [2], and streetlight illumination analysis [3], greatly promoting the future development of smart transportation [4] and digital twin [5] construction. The precise classification of static objects in road scenes from vehicle-mounted laser scanning point clouds is the focus of this study.…”

Section: Introductionmentioning

confidence: 99%

Classification of Typical Static Objects in Road Scenes Based on LO-Net

Li,

Wu,

Liu

et al. 2024

Remote Sensing

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Mobile LiDAR technology is a powerful tool that accurately captures spatial information about typical static objects in road scenes. However, the precise extraction and classification of these objects pose persistent technical challenges. In this paper, we employ a deep learning approach to tackle the point cloud classification problem. Despite the popularity of the PointNet++ network for direct point cloud processing, it encounters issues related to insufficient feature learning and low accuracy. To address these limitations, we introduce a novel layer-wise optimization network, LO-Net. Initially, LO-Net utilizes the set abstraction module from PointNet++ to extract initial local features. It further enhances these features through the edge convolution capabilities of GraphConv and optimizes them using the “Unite_module” for semantic enhancement. Finally, it employs a point cloud spatial pyramid joint pooling module, developed by the authors, for the multiscale pooling of final low-level local features. Combining three layers of local features, LO-Net sends them to the fully connected layer for accurate point cloud classification. Considering real-world scenarios, road scene data often consist of incomplete point cloud data due to factors such as occlusion. In contrast, models in public datasets are typically more complete but may not accurately reflect real-world conditions. To bridge this gap, we transformed road point cloud data collected by mobile LiDAR into a dataset suitable for network training. This dataset encompasses nine common road scene features; hence, we named it the Road9 dataset and conducted classification research based on this dataset. The experimental analysis demonstrates that the proposed algorithm model yielded favorable results on the public datasets ModelNet40, ModelNet10, and the Sydney Urban Objects Dataset, achieving accuracies of 91.2%, 94.2%, and 79.5%, respectively. On the custom road scene dataset, Road9, the algorithm model proposed in this paper demonstrated outstanding classification performance, achieving a classification accuracy of 98.5%.

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