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SCIENTIA SINICA Informationis, Volume 48 , Issue 7 : 856-870(2018) https://doi.org/10.1360/N112017-00300

Identification method for furnace flame based on adaptive color model

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  • ReceivedFeb 27, 2018
  • AcceptedMar 18, 2018
  • PublishedJul 16, 2018

Abstract


Funded by

国家自然科学基金(61633016)

上海市科委国际科技合作项目(15220710400)

上海市科委重点项目(16010500300)


References

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  • Figure 1

    (Color online) The effect diagram of the combustion flame in different scenes. Flame image (a) I, (b) II, (c) III, (d) IV

  • Figure 2

    (Color online) The 3D histograms of color components of combustion flame image I. (a) Combustion flame image I; (b) red component; (c) green component; (d) blue component

  • Figure 3

    (Color online) The 3D histograms of color components of combustion flame image II. (a) Combustion flame image II; (b) red component; (c) green component; (d) blue component

  • Figure 4

    (Color online) The 3D histograms of color components of combustion flame image III. (a) Combustion flame image III; (b) red component; (c) green component; (d) blue component

  • Figure 5

    (Color online) The 3D histograms of color components of combustion flame image IV. (a) Combustion flame image IV; (b) red component; (c) green component; (d) blue component

  • Figure 6

    The flow diagram of the flame recognition algorithm base on adaptive color model

  • Figure 7

    (Color online) Ten flame images of furnace combustion. Original image (a) I, (b) II, (c) III, (d) IV, (e) V,protect łinebreak (f) VI, (g) VII, (h) VIII, (i) IX, (j) X

  • Figure 8

    (Color online) Comparison of fitness value in original image I. (a) Genetic algorithm; (b) particle swarm optimization; (c) human learning optimization

  • Figure 9

    (Color online) Comparison of fitness value in original image II. (a) Genetic algorithm; (b) particle swarm optimization; (c) human learning optimization

  • Figure 10

    (Color online) Comparison result of flame identification in original image I. (a) Original image I; (b) Ref. [8]; (c) Ref. [11]; (d) proposed method

  • Figure 11

    (Color online) Comparison result of flame identification in original image II. (a) Original image II; (b) Ref. [8]; (c) Ref. [11]; (d) proposed method

  • Table 1   Comparison of flame identification of different algorithms
    Algorithms Threshold $t_1$ Threshold $t_2$ Fitness value Identification time (s)
    Original image I GA+proposed model 185 101 9.2733E+03 0.3748
    PSO+proposed model 178 97 9.2862E+03 0.5097
    HLO+proposed model 178 97 9.2862E+03 0.0935
    Original image II GA+proposed model 151 124 1.4230E+04 0.4757
    PSO+proposed model 160 129 1.4246E+04 0.6128
    HLO+proposed model 160 129 1.4246E+04 0.1626
  • Table 2   Comparison of flame identification in different scenes
    Method Correct pixels Total pixels Identification accuracy (%) Identification time (s)
    Original image I Ref. [8] 33222 43621 76.16 0.2747
    Ref. [11] 42651 43621 97.78 0.1702
    Proposed method 42823 43621 98.17 0.0935
    Original image II Ref. [8] 266452 284200 93.76 0.4251
    Ref. [11] 276770 284200 97.39 0.2675
    Proposed method 280521 284200 98.71 0.1626
    Original image III Ref. [8] 587651 917631 64.04 0.7643
    Ref. [11] 835341 917631 91.03 0.6568
    Proposed method 849676 917631 92.59 0.4736
    Original image IV Ref. [8] 81957 108747 75.36 0.2065
    Ref. [11] 81413 108747 74.86 0.1950
    Proposed method 101054 108747 92.93 0.0971
    Original image V Ref. [8] 670752 972873 68.95 0.9168
    Ref. [11] 963243 972873 97.77 0.7279
    Proposed method 961911 972873 98.87 0.5164
    Original image VI Ref. 8 474027 782595 60.57 0.7238
    Ref. 11 759863 782595 97.10 0.5636
    Proposed method 774106 782595 98.92 0.3556
    Original image VII Ref. [8] 23141 28400 81.48 0.1513
    Ref. [11] 20451 28400 72.01 0.1595
    Proposed method 27549 28400 97.00 0.0813
    Original image VIII Ref. [8] 92974 97660 95.20 0.1765
    Ref. [11] 89838 97660 91.99 0.1810
    Proposed method 94374 97660 96.64 0.0977
    Original image IX Ref. [8] 539152 735232 73.33 0.6835
    Ref. [11] 393618 735232 94.34 0.5329
    Proposed method 701257 735232 95.38 0.3324
    Original image X Ref. [8] 22704 24928 91.08 0.1305
    Ref. [11] 19890 24928 79.79 0.1596
    Proposed method 23973 24928 96.17 0.0796