• Laser & Optoelectronics Progress
  • Vol. 61, Issue 20, 2011014 (2024)
Wanyuan Cai1,*, Bin Shen2, Zhenyu Li1, Te Wen1, and Wei Tao1
Author Affiliations
  • 1School of Sensing Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
  • 2Shanghai Satellite Engineering Research Institute, Shanghai 201109, China
  • show less
    DOI: 10.3788/LOP241571 Cite this Article Set citation alerts
    Wanyuan Cai, Bin Shen, Zhenyu Li, Te Wen, Wei Tao. Line-Laser-Based Method for Road Pavement Roughness Detection (Invited)[J]. Laser & Optoelectronics Progress, 2024, 61(20): 2011014 Copy Citation Text show less
    References

    [1] Loprencipe G, Cantisani G, Di Mascio P. Global assessment method of road distresses[M]. Life-cycle of structural systems, 1113-1120(2014).

    [2] Liu Q H, Zhou W, He R et al. Road roughness recognition based on improved fuzzy C-mean algorithm combined with genetic algorithm[J]. Transactions of the Chinese Society of Agricultural Engineering, 30, 195-200(2014).

    [3] Du Y C, Liu C L, Wu D F et al. Pavement roughness measurement method based on automobile mounted multiple sensors[J]. China Journal of Highway and Transport, 28, 1-5(2015).

    [4] Jiang D, Liu X K. Road flatness detection based on magnetic levitation vibration measurement technique[J]. Instrument Technique and Sensor, 102-106(2017).

    [5] Zhan Y, Au F T K. Bridge surface roughness identification based on vehicle-bridge interaction[J]. International Journal of Structural Stability and Dynamics, 19, 1950069(2019).

    [6] Jeong J H, Jo H, Ditzler G. Convolutional neural networks for pavement roughness assessment using calibration-free vehicle dynamics[J]. Computer-Aided Civil and Infrastructure Engineering, 35, 1209-1229(2020).

    [7] Liu C L, Wu D F, Li Y S et al. Large-scale pavement roughness measurements with vehicle crowdsourced data using semi-supervised learning[J]. Transportation Research Part C: Emerging Technologies, 125, 103048(2021).

    [8] Wu B J, Liu D H, Sun Y Z et al. Pavement roughness calculation of entire road surface based on automatic road elevation measuring[J]. China Journal of Highway and Transport, 29, 10-17(2016).

    [9] Alhasan A, White D J, De Brabanter K. Quantifying roughness of unpaved roads by terrestrial laser scanning[J]. Transportation Research Record, 2523, 105-114(2015).

    [10] Kumar P, Angelats E. An automated road roughness detection from mobile laser scanning data[J]. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 91-96(2017).

    [11] Khalifeh V, Golroo A, Ovaici K. Application of an inexpensive sensor in calculating the international roughness index[J]. Journal of Computing in Civil Engineering, 32, 04018022(2018).

    [12] Mahmoudzadeh A, Golroo A, Jahanshahi M R et al. Estimating pavement roughness by fusing color and depth data obtained from an inexpensive RGB-D sensor[J]. Sensors, 19, 1655(2019).

    [13] de Blasiis M R, Di Benedetto A, Fiani M et al. Assessing of the road pavement roughness by means of LiDAR technology[J]. Coatings, 11, 17(2020).

    [14] Wang Z Y, Liu H L, Liu W. Geometric dimension measurement method for bolster spring based on three-dimensional laser point clouds[J]. Chinese Journal of Lasers, 50, 1904001(2023).

    [15] Lai Y, Wang J D, Guo H R et al. Online detection method for metro pantograph wear based on line-laser measurement[J]. Chinese Journal of Lasers, 50, 2304001(2023).

    [16] Wu Q H, Qiu J F, Li Z A et al. Hand-eye calibration method of line structured light vision sensor robot based on planar target[J]. Laser & Optoelectronics Progress, 60, 1015002(2023).

    [17] Wu G Q, Yu J Y, Ma W et al. Construction building flatness detection method based on 3D laser scanning[J]. Laser & Optoelectronics Progress, 60, 1612004(2023).

    [18] Zhang Z Y. Flexible camera calibration by viewing a plane from unknown orientations[C], 666-673(1999).

    [19] Steger C. An unbiased detector of curvilinear structures[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20, 113-125(1998).