RdmkNet & Toronto-RDMK: Large-Scale Datasets for Road Marking Classification and Segmentation

Effective road marking classification and segmentation play a pivotal role in advancing vehicle-to-everything (V2X) applications and refining road inventory databases. However, the irregular data formats and unordered permutation modes of 3D point clouds, along with the limited availability of large-scale datasets with point-level annotations, remain significant obstacles to designing deep learning-based networks with superior performance. To address these challenges, this paper proposes a novel multi-level feature optimization network structure, named MFPNet, and introduces two point cloud benchmarks, RdmkNet and Toronto-Rdmk, for road marking classification and segmentation in intricate urban environments. MFPNet is composed of three integral modules. First, the M-transformer module, consisting of three transformers obtained from different channels, fully captures rich point cloud background information and long-distance dependencies between objects. Then, the feature pooling aggregation module uses parallel structured pooling attention mechanisms to aggregate features captured by the M-transformer module, while the prediction refinement module further enhances the acquisition of semantic features. Comparative studies indicate that MFPNet can be embedded into general deep learning networks without changing their original network structures, significantly improving the accuracy of multiple baseline networks. Furthermore, extensive experiments demonstrate that the two newly-developed point cloud datasets are meaningful for road marking classification and segmentation tasks, contributing to the development of autonomous driving.

• Record URL:
  • http://dx.doi.org/10.1109/TITS.2024.3394481
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2024, IEEE.
• Authors:
  • Du, J
  • Ma, L
  • Qin, N
  • Zelek, J
  • Guan, H
  • Li, Jun
• Publication Date: 2024-10

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 13467-13482
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 25
  • Issue Number: 10
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Computer networks; Databases; Machine learning; Point clouds; Road markings; Structural health monitoring; Vehicle to everything communications
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01942304
• Record Type: Publication
• Files: TRIS
• Created Date: Jan 13 2025 10:32AM