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Laser & Optoelectronics Progress, Volume. 57, Issue 12, 121009(2020)

Gear Defect Detection Based on the Improved YOLOv3 Network

Guangshi Zhang1, Guangying Ge1、*, Ronghua Zhu1, and Qun Sun2
Author Affiliations
  • 1College of Physics and Information Engineering, Liaocheng University, Liaocheng, Shandong 252059, China
  • 2College of Mechanical and Automotive Engineering, Liaocheng University, Liaocheng, Shandong 252059, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (8)
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    References (20)
    Cited By (9)
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    Figures & Tables(8)
    YOLOv3 network structure

    Fig. 1. YOLOv3 network structure

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    Improved network structure

    Fig. 2. Improved network structure

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    Loss function and IOU curves of train dataset of YOLOv3_Dense4 network

    Fig. 3. Loss function and IOU curves of train dataset of YOLOv3_Dense4 network

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    Loss function and IOU curves of test dataset of YOLOv3_Dense4 network

    Fig. 4. Loss function and IOU curves of test dataset of YOLOv3_Dense4 network

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    Defection results of defect gear. (a) Detection results of gears with different defects; (b) detection results of defect gears and flawless gears

    Fig. 5. Defection results of defect gear. (a) Detection results of gears with different defects; (b) detection results of defect gears and flawless gears

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    Detection results of gear defects under different light intensities. (a) 249.28 lx; (b) 321.61 lx; (c) 394.93 lx

    Fig. 6. Detection results of gear defects under different light intensities. (a) 249.28 lx; (b) 321.61 lx; (c) 394.93 lx

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    • Table 1. Performance comparison of different methods

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      Table 1. Performance comparison of different methods

      NetClassNumber of defectsPTPPFPPFNP /%RmAP /%R /%F1 /%Time /s
      YOLOv3Stain786725216197.1894.5892.2494.650.098
      Miss5724513912591.9878.1584.50
      YOLOv3_Dense3Stain786756103098.6997.6296.1897.420.107
      Miss572475179796.5483.0489.29
      YOLOv3_Dense4Stain78676562199.2298.4597.3398.270.104
      Miss572547132597.6895.6396.64

    • Table 2. Performance comparison of YOLOv3_Dense4 under different light intensities

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      Table 2. Performance comparison of YOLOv3_Dense4 under different light intensities

      Light intensity /lxClassNumber of defectsPTPPFPPFNP /%RmAP /%R /%F1 /%
      394.93Stain2562484898.4197.8096.8897.64
      Miss21820861097.2095.4196.30
      321.61Stain2562473998.8098.0096.4897.63
      Miss2182096997.2195.8796.54
      249.28Stain25624131598.7798.2194.1496.40
      Miss21820751197.6494.9596.28
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    Guangshi Zhang, Guangying Ge, Ronghua Zhu, Qun Sun. Gear Defect Detection Based on the Improved YOLOv3 Network[J]. Laser & Optoelectronics Progress, 2020, 57(12): 121009

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    Paper Information

    Category: Image Processing

    Received: Aug. 30, 2019

    Accepted: Oct. 31, 2019

    Published Online: Jun. 3, 2020

    The Author Email: Guangying Ge (406381534@qq.com)

    DOI:10.3788/LOP57.121009

    Topics

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