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Laser & Optoelectronics Progress, Volume. 59, Issue 18, 1815011(2022)

Sorting and Detection of Impurity Glass Based on YOLOv4

Bo Yang1, Zhenming Xu1、*, and Jianxin Liu2
Author Affiliations
  • 1China-UK Low Carbon College, Shanghai Jiao Tong University, Shanghai 201306, China
  • 2Nanjing Institute of Electronic Technology, Nanjing 210039, Jiangsu , China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (12)
    Equations (8)
    References (19)
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    Paper Information
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    Figures & Tables(12)
    Impurity glass samples. (a) Crystal; (b) glue; (c) laminated; (d) bar

    Fig. 1. Impurity glass samples. (a) Crystal; (b) glue; (c) laminated; (d) bar

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    Three labelling methods for bar and copying. (a) Horizontal box; (b) rotated box; (c) square boxes; (d) small object copying

    Fig. 2. Three labelling methods for bar and copying. (a) Horizontal box; (b) rotated box; (c) square boxes; (d) small object copying

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    Relationship between clustering effect of Kmeans++and k

    Fig. 3. Relationship between clustering effect of Kmeans++and k

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    Clustering result of Kmeans++ algorithm

    Fig. 4. Clustering result of Kmeans++ algorithm

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    YOLOv4 network structure and adjustment

    Fig. 5. YOLOv4 network structure and adjustment

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    Loss and AP of validation set during training

    Fig. 6. Loss and AP of validation set during training

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    Calculation method of occlusion proportion

    Fig. 7. Calculation method of occlusion proportion

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    AP under different augmenting types before & after training

    Fig. 8. AP under different augmenting types before & after training

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    Target pasting and detecting examples. (a)(b) Target attached with label; (c) interference; (d) occlusion

    Fig. 9. Target pasting and detecting examples. (a)(b) Target attached with label; (c) interference; (d) occlusion

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    • Table 1. Accuracy comparison of different models in testset

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      Table 1. Accuracy comparison of different models in testset

      MethodAPmAP
      BarCrystalGlueLaminated
      YOLOv488.7797.3198.9698.0495.76
      YOLOv4+93.9698.2699.9798.0497.55
      YOLOV4_t+95.4798.9899.0797.9997.88

    • Table 2. Positive and negative sample distribution

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      Table 2. Positive and negative sample distribution

      Type of boxesBarCrystalGlueLaminated
      GT262117110102
      TP25511610999
      FP1992221

    • Table 3. Parameter size and detection performance of typical networks

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      Table 3. Parameter size and detection performance of typical networks

      ParameterYOLOv4YOLOv3YOLOv4-tinyYOLOv3-tinyYOLOv4_t
      mAP0.960.920.880.860.98
      Speed/（frame·s-1）40.5438.51118.67158.2542.82
      Multi-Add/10929.7832.663.3928.96
      Params/106255.82246.1623.5217.90176.22
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    Bo Yang, Zhenming Xu, Jianxin Liu. Sorting and Detection of Impurity Glass Based on YOLOv4[J]. Laser & Optoelectronics Progress, 2022, 59(18): 1815011

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    Paper Information

    Category: Machine Vision

    Received: Jun. 15, 2021

    Accepted: Aug. 10, 2021

    Published Online: Aug. 29, 2022

    The Author Email: Zhenming Xu (zmxu@sjtu.edu.cn)

    DOI:10.3788/LOP202259.1815011

    Topics

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