Chinese Journal of Lasers, Volume. 52, Issue 11, 1110001(2025)

Image Processing-Based Method for Detecting Underwater Obstacles with Airborne Lidar

Hao Wang1,2, Yan He1,2、*, Deliang Lü1,2, Chunhe Hou1,2, Sheng Su1, Pengrui Liang1, Xinke Hao1,2, and Yujie Chen1,3
Author Affiliations
  • 1Wangzhijiang Innovation Center for Laser, Aerospace Laser Technology and System Department, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China
  • 2Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
  • 3College of Information Science and Technology, Donghua University, Shanghai 201620, China
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    Figures & Tables(10)
    Image assembled by stitching waveforms. (a) Example of lidar echo waveform stitching; (b) echo waveform containing surface water, subsurface water, and underwater obstacles; (c) echo waveform containing surface water and subsurface water
    Comparison of images before and after correction. (a) Example of image before correction; (b) example of corrected image
    Comparison of waveforms with and without water scattering removal for the obstacle located in the water scattering region
    Comparisons of contrast in obstacle and background areas before and after water scattering removal. (a) Contrast calculation area in image before water scattering removal; (b) contrast calculation area in image after water scattering removal; (c) contrast between obstacle and background areas before water scattering removal; (d) contrast between obstacle and background areas after water scattering removal
    Comparison before and after replacing local bright stripes. (a) Local image before replacing local bright stripes; (b) local image after replacing local bright stripes; (c) horizontal gradient map before replacing local bright stripes; (d) horizontal gradient map after replacing local bright stripes
    DoG-ECD method process
    Canny operator detection results. (a) Global image; (b) local image
    Experimental results of image processing. (a)(d)(g)(j) Input images; (b)(e)(h)(k) optimal scale corresponding to input images; (c)(f)(i)(l) obstacle area and connected domain labels corresponding to input images
    • Table 1. Lidar parameter

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      Table 1. Lidar parameter

      ParameterDescription
      TransmitterWavelength532.1 nm
      Laser repetition rate2000 Hz
      Pulse energy0.1 mJ
      Pulse width1.9 ns
      ReceiverTelescope diameter50 mm
      DetectorPMT
      Detector efficiency0.13
      Sampling rate1 GHz
    • Table 2. Detection accuracy of Canny operator and DoG-ECD method for images with obstacles

      View table

      Table 2. Detection accuracy of Canny operator and DoG-ECD method for images with obstacles

      MethodAccuracy /%
      GlobalLocal
      Canny operator62.6476.92
      DoG-ECD method93.40
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    Hao Wang, Yan He, Deliang Lü, Chunhe Hou, Sheng Su, Pengrui Liang, Xinke Hao, Yujie Chen. Image Processing-Based Method for Detecting Underwater Obstacles with Airborne Lidar[J]. Chinese Journal of Lasers, 2025, 52(11): 1110001

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    Paper Information

    Category: remote sensing and sensor

    Received: Jan. 13, 2025

    Accepted: Mar. 14, 2025

    Published Online: Jun. 13, 2025

    The Author Email: Yan He (heyan@siom.ac.cn)

    DOI:10.3788/CJL250465

    CSTR:32183.14.CJL250465

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