Laser & Optoelectronics Progress, Volume. 59, Issue 14, 1415026(2022)

Three-Dimensional Pavement Texture Information Acquisition Based on Binocular Vision Algorithm

Rong Jiang1,2,3, Pan Zhu1,2,3、*, Xinglin Zhou1,2,3, and Lu Liu1,2,3
Author Affiliations
  • 1School of Machinery and Automation, Wuhan University of Science and Technology, Wuhan 430081, Hubei , China
  • 2Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, Hubei , China
  • 3Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, Hubei , China
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    The three-dimensional (3D) texture information of asphalt pavement is important information for characterizing its skid resistance. To mitigate the problem of interference to information acquisition due to the high matching error rate in the weak-textured and non-textured areas of the asphalt pavement, this paper proposes a pavement 3D texture information acquisition method using a binocular vision stereo matching algorithm. First, the binocular vision measurement platform is built, and the internal and external parameters of the binocular camera are obtained using Zhang Zhengyou’s checkerboard calibration method. Second, for the digital images collected by the binocular camera, the stereo matching algorithm that introduces the cross-scale cost aggregation model is used to get a better parallax map. Finally, we obtain the 3D model of the pavement texture using reverse reconstruction, and then the pavement texture information is obtained. The experimental results show that the proposed method reconstructs a more accurate 3D model of the road surface with the relative error within 5% and has a high accuracy and a good robustness for obtaining road texture information.

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    Rong Jiang, Pan Zhu, Xinglin Zhou, Lu Liu. Three-Dimensional Pavement Texture Information Acquisition Based on Binocular Vision Algorithm[J]. Laser & Optoelectronics Progress, 2022, 59(14): 1415026

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    Paper Information

    Category: Machine Vision

    Received: Jul. 30, 2021

    Accepted: Oct. 13, 2021

    Published Online: Jul. 1, 2022

    The Author Email: Zhu Pan (zhuyangpp@163.com)

    DOI:10.3788/LOP202259.1415026

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