• Laser & Optoelectronics Progress
  • Vol. 59, Issue 2, 0210020 (2022)
Qinghua Gu1、2、*, Fawen Wei1、2, Mengli Guo1、2, Song Jiang1、2, and Shunling Ruan1、2
Author Affiliations
  • 1School of Resource Engineering, Xi'an University of Architecture and Technology, Xi'an , Shaanxi 710055, China
  • 2Xi'an Key Laboratory of Smart Industry Perception Computing and Decision Making, Xi'an , Shaanxi 710055, China
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    DOI: 10.3788/LOP202259.0210020 Cite this Article Set citation alerts
    Qinghua Gu, Fawen Wei, Mengli Guo, Song Jiang, Shunling Ruan. Segmentation Method of Broken Ore Image Based on Improved HED Network Model[J]. Laser & Optoelectronics Progress, 2022, 59(2): 0210020 Copy Citation Text show less
    Structure of VGG network
    Fig. 1. Structure of VGG network
    Structure of deformable convolution module
    Fig. 2. Structure of deformable convolution module
    Schematic of convolution sampling points. (a) Standard convolution; (b) deformable convolution
    Fig. 3. Schematic of convolution sampling points. (a) Standard convolution; (b) deformable convolution
    Expansion results of convolution kernel of 3×3 size under different cavity rates. (a) l=1; (b) l=2; (c) l=3
    Fig. 4. Expansion results of convolution kernel of 3×3 size under different cavity rates. (a) l=1; (b) l=2; (c) l=3
    Framework structure of proposed network
    Fig. 5. Framework structure of proposed network
    Images before and after bilateral filtering. (a) Original image of ore; (b) image filtered by 3×3 filtering window
    Fig. 6. Images before and after bilateral filtering. (a) Original image of ore; (b) image filtered by 3×3 filtering window
    Relationship among loss, accuracy, and iterations of improved HED network model. (a) Loss; (b) accuracy
    Fig. 7. Relationship among loss, accuracy, and iterations of improved HED network model. (a) Loss; (b) accuracy
    Segmentation results of different network models. (a) Original images; (b) Canny operator; (c) original HED network; (d) improved HED network
    Fig. 8. Segmentation results of different network models. (a) Original images; (b) Canny operator; (c) original HED network; (d) improved HED network
    ParameterValue
    Number of calculations300
    Iterations100
    Number of images per iteration4
    Number of training samples1000
    Table 1. Training parameters of model
    ImageAccuracyRecallPrecision
    CannyHEDImproved HEDCannyHEDImproved HEDCannyHEDImproved HED
    10.72830.81720.91830.81620.84380.82750.87540.92190.9428
    20.78820.86350.91170.82250.80350.81730.88290.91780.9537
    30.79370.88790.92250.81560.81670.81580.84780.93780.9389
    40.70140.89180.93080.80680.81240.80670.85130.92470.9409
    50.76910.88150.91260.79950.80370.81170.82180.93450.9128
    60.74370.88570.92150.80930.79690.80270.81450.93620.9390
    70.76590.89720.91180.81090.80890.81640.80470.94190.9268
    80.75730.80270.91690.80380.81290.80580.84390.93700.9503
    90.76540.88960.90270.79950.80970.81870.80570.91050.9218
    100.77150.88890.90470.80920.81260.82130.81200.92790.9348
    Average0.75850.87060.91540.80930.81210.81440.83600.92900.9362
    Table 2. Performance indicators of different networks
    ModelMIOU /%Time /ms
    Canny72.62128
    HED76.4998
    Improved HED77.2381
    Table 3. Time comparison results of different networks
    ExperimentDCNDCAP /%
    1××85.47
    2×90.23
    392.37
    Table 4. Ablation experiment results of different network models
    Qinghua Gu, Fawen Wei, Mengli Guo, Song Jiang, Shunling Ruan. Segmentation Method of Broken Ore Image Based on Improved HED Network Model[J]. Laser & Optoelectronics Progress, 2022, 59(2): 0210020
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