Remote Sensing Technology and Application, Volume. 40, Issue 3, 659(2025)

Oblique Photogrammetry-based Traffic Sign Embedded Modeling

Qian MA1, Yunlong GAO2,3, Suole LI1, Lijun FU1, Yufeng HUANG1, and Zhu MAO2、*
Author Affiliations
  • 1Inner Mongolia Autonomous Region Surveying, Mapping, and Geographic Information Center, Hohhot010050, China
  • 2State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan430079, China
  • 3Wuhan Daspatial Technology Company Limited, Wuhan430070, China
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    Figures & Tables(12)
    Texture and 3D model database of traffic sign
    Data synthesis based on 3D model transformation
    Data synthesis based on 3D model transformation
    Overall framework of the proposed method
    Deep learning-based neural network for traffic sign detection
    Corresponding point extraction with geometric constraints
    Deep learning-based neural network for traffic sign detection
    Traffic sign detection results
    3D point clouds of traffic sign generated by triangulation
    Traffic sign embedded modeling results
    • Table 1. Quotative results traffic sign detection

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      Table 1. Quotative results traffic sign detection

      方法指示标志警告标志禁止标志mAP
      Faster0.8030.7850.9090.832
      Faster with SD0.9070.8860.9030.897
      本研究方法0.9090.9870.9090.935
    • Table 2. Quotative results of triangulation

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      Table 2. Quotative results of triangulation

      方法RMSE(像素)MAE(像素)
      三角化2.932.359
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    Qian MA, Yunlong GAO, Suole LI, Lijun FU, Yufeng HUANG, Zhu MAO. Oblique Photogrammetry-based Traffic Sign Embedded Modeling[J]. Remote Sensing Technology and Application, 2025, 40(3): 659

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    Paper Information

    Category:

    Received: Apr. 1, 2024

    Accepted: --

    Published Online: Sep. 28, 2025

    The Author Email: Zhu MAO (maoz@whu.edu.cn)

    DOI:10.11873/j.issn.1004-0323.2025.3.0659

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