Acta Photonica Sinica, Volume. 50, Issue 10, 1011002(2021)

Cognitive Imaging Lidar Based on Deep Learning(Invited)

Shuai YUAN, Xiang YAN, Jingxian XU, Wenrui ZHU, and Hanlin QIN*
Author Affiliations
  • School of Physics and Optoelectronic Engineering,Xidian University,Xi'an 710071,China
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    Figures & Tables(12)
    Operating principle of cognitive imaging lidar based on deep learning
    Block diagram of module internal structure
    Comparison of object detection results under different laser power in the same scene
    Scanning structure and scanning field of view control model
    Object detection results under different scanning resolutions in the same scene
    Flow chart of cognitive feedback for deep learning
    Adaptive adjustment strategy of cognitive scanning field of view for deep learning
    Comparison of conventional model detection result and cognitive feedback detection result
    Comparison of target detection effects between conventional model and cognitive feedback in remote multi-object scene
    Comparison of target detection effects between traditional mode and cognitive feedback in long-range target squint scene
    • Table 1. Laser source parameters

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      Table 1. Laser source parameters

      WavelengthPulse durationPulse repetition frequencyAverage powerPeak powerDivergenceSpot diameter
      1.55 μm10 ns50 kHz25 W1 kW0.351 mrad12.2 mm
    • Table 2. Comparison of recall and precision of car targets under traditional mode and cognitive mode

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      Table 2. Comparison of recall and precision of car targets under traditional mode and cognitive mode

      MethodRecall/%Precision/%
      ConventionalCognitiveConventionalCognitive
      SECOND71.6276.5854.8865.23
      PointPillars72.9377.3155.4665.88
      CenterPoint75.2180.0254.3164.67
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    Shuai YUAN, Xiang YAN, Jingxian XU, Wenrui ZHU, Hanlin QIN. Cognitive Imaging Lidar Based on Deep Learning(Invited)[J]. Acta Photonica Sinica, 2021, 50(10): 1011002

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    Paper Information

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    Received: Jul. 29, 2021

    Accepted: Sep. 14, 2021

    Published Online: Nov. 3, 2021

    The Author Email: QIN Hanlin (hlqin@mail.xidian.edu.cn)

    DOI:10.3788/gzxb20215010.1011002

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