Infrared and Laser Engineering, Volume. 53, Issue 7, 20240133(2024)

LiDAR profile image processing method for underwater obstacle

Yingjie RUAN1,2,3, Yan HE1,2,3, Deliang LV1,2,3, Chunhe HOU1,2,3, Guangxiu XU4, Chaoran ZHANG4, Yifan HUANG1,2,3, and Xinke HAO1,2,3
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
  • 2Key Laboratory of Space Laser Communication and Detection Technology, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China
  • 3Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Science, Beijing 100049, China
  • 4Naval Research Institute, Tianjin 300061, China
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    Figures & Tables(18)
    Unmanned airborne ocean LiDAR
    Schematic diagram of the receiving optical path
    Photograph of the obstacle
    Multi-channel waveform data obtained by airborne experiment
    Echo energy profile
    Local echo energy profiles of underwater obstacle
    Schematic diagram of the principle of linear-approximation of leading edge algorithm
    Modeling of laser emission angle
    Echo energy profile after angle correction
    Histogram of water surface height statistics
    Local echo energy profile of underwater obstacle after angle correction
    Obstacle echo profile edge extraction results of adjacent scan lines
    Depth of the centroid of obstacle contours
    The 20 sample images used for validating automatic recognition
    Schematic diagram of the water background region extraction
    • Table 1. Parameters of the LiDAR

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      Table 1. Parameters of the LiDAR

      ParameterValue
      Wavelength/nm532.1
      Pulse energy/mJ0.1
      Laser frequency/Hz2000
      Pulse width/ns1.9
      Divergence angle/mrad2.4
      Receiving diameter/mm50
      Field of view/mrad90
      Sample rate/ns1
    • Table 2. Image contour similarity

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      Table 2. Image contour similarity

      Image labelSimilarity
      Fig.12(b)Fig.12(c)Fig.12(d)Fig.12(e)Fig.12(f)
      Fig.12(a)99.52%99.27%98.38%99.86%99.92%
      Fig.12(b)99.97%99.66%98.86%99.06%
      Fig.12(c)99.82%98.50%98.72%
      Fig.12(d)97.29%97.60%
      Fig.12(e)99.99%
    • Table 3. Variance of the amplitude in test samples

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      Table 3. Variance of the amplitude in test samples

      Image No.$ {s}^{2} $Image No.$ {s}^{2} $
      Fig.14(a)28.47Fig.14(k)235.43
      Fig.14(b)36.18Fig.14(l)245.75
      Fig.14(c)65.63Fig.14(m)276.09
      Fig.14(d)148.38Fig.14(n)311.97
      Fig.14(e)147.55Fig.14(o)240.61
      Fig.14(f)173.46Fig.14(p)159.66
      Fig.14(g)257.78Fig.14(q)117.99
      Fig.14(h)203.27Fig.14(r)109.83
      Fig.14(i)332.11Fig.14(s)27.85
      Fig.14(j)257.93Fig.14(t)21.63
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    Yingjie RUAN, Yan HE, Deliang LV, Chunhe HOU, Guangxiu XU, Chaoran ZHANG, Yifan HUANG, Xinke HAO. LiDAR profile image processing method for underwater obstacle[J]. Infrared and Laser Engineering, 2024, 53(7): 20240133

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    Paper Information

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    Received: Mar. 25, 2024

    Accepted: --

    Published Online: Aug. 9, 2024

    The Author Email:

    DOI:10.3788/IRLA20240133

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