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Laser & Optoelectronics Progress, Volume. 59, Issue 16, 1615002(2022)

Fish Recognition and Detection Based on FML-Centernet Algorithm

Yuqing Liu1,2, Yaru Wang1,2、*, and Luyao Huang1,2
Author Affiliations
  • 1College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, China
  • 2Shanghai Engineering Research Center of Marine Renewable Energy, Shanghai 201306, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (12)
    Equations (7)
    References (18)
    Cited By (1)
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    Paper Information
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    Figures & Tables(12)
    Network model structure of proposed algorithm

    Fig. 1. Network model structure of proposed algorithm

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    Network structure of FFM

    Fig. 2. Network structure of FFM

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    Image space geometric transformation. (a) Original image; (b) mirror image; (c) flip; (d) spin

    Fig. 3. Image space geometric transformation. (a) Original image; (b) mirror image; (c) flip; (d) spin

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    Label data

    Fig. 4. Label data

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    Target dataset

    Fig. 5. Target dataset

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    Loss function curve of Centernet

    Fig. 6. Loss function curve of Centernet

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    Loss function curve of FMCenternet

    Fig. 7. Loss function curve of FMCenternet

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    Loss function curve of FML-Centernet

    Fig. 8. Loss function curve of FML-Centernet

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    Partial recognition results. (a) (b) Faster-RCNN; (c) (d) YOLOV4; (e) (f) FML-Centernet

    Fig. 9. Partial recognition results. (a) (b) Faster-RCNN; (c) (d) YOLOV4; (e) (f) FML-Centernet

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    • Table 1. Comparative analysis experiments of different algorithms

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      Table 1. Comparative analysis experiments of different algorithms

      AlgorithmAP75AP50AP
      Centernet28.6579.2138.03
      FMCenternet31.5181.3637.98
      FML-Centernet42.5185.9943.91

    • Table 2. Comparative analysis of experimental results of different network models

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      Table 2. Comparative analysis of experimental results of different network models

      Network modelNetwork structureAP50 /%Average detection time /ms
      Faster-RCNNVGG74.32221.35
      YOLOV4CSPDarknet5376.95185.46
      CenternetResNet5079.21178.69
      FMCenternetResNet50+FFM81.36140.23
      FML-CenternetResNet50+FFM+LOSS85.9999.85

    • Table 3. Verification results of different network models on target test set

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      Table 3. Verification results of different network models on target test set

      Network modelFishAP50 %Average detection time /ms
      Faster-RCNNSPT73.82220.36
      Pomfret74.21218.23
      Linnaeus73.34219.34
      CT74.53215.26
      DF74.15217.35
      YOLOV4SPT76.96180.52
      Pomfret76.52181026
      Linnaeus76.03184.37
      CT77.01183.45
      DF76.86187.25
      FML-CenternetSPT85.45100.21
      Pomfret86.1398.36
      Linnaeus86.0298.45
      CT85.8696.15
      DF86.2497.69
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    Yuqing Liu, Yaru Wang, Luyao Huang. Fish Recognition and Detection Based on FML-Centernet Algorithm[J]. Laser & Optoelectronics Progress, 2022, 59(16): 1615002

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    Paper Information

    Category: Machine Vision

    Received: Apr. 30, 2021

    Accepted: Jul. 1, 2021

    Published Online: Jul. 22, 2022

    The Author Email: Yaru Wang (2432526810@qq.com)

    DOI:10.3788/LOP202259.1615002

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology