Acta Optica Sinica, Volume. 40, Issue 24, 2411001(2020)

Coal Gangue Detection Based on Multi-Spectral Imaging and Improved YOLO v4

Wenhao Lai, Mengran Zhou*, Feng Hu, Kai Bian, and Hongping Song
Author Affiliations
  • College of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, Anhui 232000, China
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    Figures & Tables(13)
    System of multi-spectral data acquisition
    Multi-spectral image of coal and coal gangue
    Structures of YOLO and YOLO v3 models
    Schematic diagram of YOLO v4.1
    Average recognition accuracy in different bands. (a) Two classes of coal and coal gangue; (b) three classes of coal, coal gangue, and mix
    Confusion matrix of correlation coefficients
    Test results. (a) Coal416; (b) coal512; (c) coal608; (d) coal408; (e) coal gangue416; (f) coal gangue512; (g) coal gangue608; (h) coal gangue408
    Detection result of YOLO v4.1
    • Table 1. Data information of coal and coal gangue

      View table

      Table 1. Data information of coal and coal gangue

      SampleNumber of lumpsTotal
      123
      Coal1757550300
      Coal gangue1757550300
      Mix-100150250
    • Table 2. Recognition results of AdaBoost, RF, and CART

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      Table 2. Recognition results of AdaBoost, RF, and CART

      AlgorithmCoal and coal gangueCoal, coal gangue, and mix
      Maximum average accuracy /%BandMaximum average accuracy /%Band
      RF97.86,7,1087.09
      CART89.01370.89
      AdaBoost98.01277.32,6
    • Table 3. [in Chinese]

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      Table 3. [in Chinese]

      Band971168101321
      Average accuracy87.086.786.786.486.486.486.486.4
    • Table 4. Test results of each detection model

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      Table 4. Test results of each detection model

      Detection modelInput resolution /(pixel×pixel)Average precisionmAP /%Test time /s
      Coal /%Coal gangue /%
      YOLO v4.1408×40898.7397.7898.264.18
      416×41697.4793.4995.483.43
      YOLO v4512×51298.6193.8796.244.33
      608×60898.5997.0497.816.07
    • Table 5. Coordinate and score of bounding box

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      Table 5. Coordinate and score of bounding box

      SampleDetection result [label:score (xlt, ylt), (xrb, yrb)]
      LabelScore(xlt,ylt),(xrb,yrb)LabelScore(xlt,ylt),(xrb,yrb)LabelScore(xlt,ylt),(xrb,yrb)
      1c0.97(87,124),(173,254)--
      2c1.00(8,200),(109,275)c1.00(92,221),(184,297)-
      3c1.00(34,119),(130,209)c0.99(42,217),(173,319)c0.91(120,104),(216,223)
      4g0.58(90,145),(216,261)--
      5g1.00(43,242),(152,326)g0.98(51,209),(149,266)-
      6g0.84(7,178),(162,320)g0.81(79,152),(164,255)-
      7g1.00(6,110),(141,205)c0.72(44,205),(173,318)-
      8g0.96(29,100),(185,197)c0.99(41,202),(126,320)-
      9g0.98(106,134),(216,258)c0.99(26,84),(128,196)c0.99(29,212),(124,326)
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    Wenhao Lai, Mengran Zhou, Feng Hu, Kai Bian, Hongping Song. Coal Gangue Detection Based on Multi-Spectral Imaging and Improved YOLO v4[J]. Acta Optica Sinica, 2020, 40(24): 2411001

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    Paper Information

    Category: Imaging Systems

    Received: Jun. 28, 2020

    Accepted: Sep. 8, 2020

    Published Online: Nov. 23, 2020

    The Author Email: Zhou Mengran (mrzhou8521@163.com)

    DOI:10.3788/AOS202040.2411001

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