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Review of Intelligent Road Defects Detection Technology

Author

Listed:
  • Yong Zhou

    (Shandong Hi-Speed Construction Management Group Co., Ltd., Jinan 250014, China)

  • Xinming Guo

    (School of Qilu Transportation, Shandong University, Jinan 250002, China)

  • Fujin Hou

    (Shandong Hi-Speed Construction Management Group Co., Ltd., Jinan 250014, China)

  • Jianqing Wu

    (School of Qilu Transportation, Shandong University, Jinan 250002, China
    Suzhou Research Institute, Shandong University, Suzhou 215000, China)

Abstract

Road defects are important factors affecting traffic safety. In order to improve the identification efficiency of road diseases and the pertinence of maintenance and management, intelligent detection technologies of road diseases have been developed. The problems of high cost and low efficiency of artificial inspection of road diseases are solved efficiently, and the quality of road construction is improved availably. This is not only the guarantee of highway quality but also the guarantee of people’s lives and safety. This study focuses on the intelligent detection of road disease and summarizes the commonly used detection equipment in the intelligent detection technology of road diseases, which include cameras, GPR, LiDAR, and IMU. It systematically describes the evolution and development of road disease detection technology. This study analyzes the common problems existing in road disease detection technology and proposes corresponding improvement suggestions. Finally, the development trend of road detection technology is discussed, which has practical significance for the future development of road detection technology.

Suggested Citation

  • Yong Zhou & Xinming Guo & Fujin Hou & Jianqing Wu, 2022. "Review of Intelligent Road Defects Detection Technology," Sustainability, MDPI, vol. 14(10), pages 1-19, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:10:p:6306-:d:821086
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    References listed on IDEAS

    as
    1. Baskerville, Nicholas P. & Granziol, Diego & Keating, Jonathan P., 2022. "Appearance of Random Matrix Theory in deep learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 590(C).
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