• Laser & Optoelectronics Progress
  • Vol. 58, Issue 2, 0210004 (2021)
Jun Deng1, Hanwen Yao1、2, Weifeng Wang1、*, Zhao Li1、2, and Ce Liang2
Author Affiliations
  • 1School of Electrical and Control Engineering, Xi'an University of Science and Technology, Xi'an, Shaanxi 710054, China
  • 2School of Safety Science and Engineering, Xi'an University of Science and Technology, Xi'an, Shaanxi 710054, China
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    DOI: 10.3788/LOP202158.0210004 Cite this Article Set citation alerts
    Jun Deng, Hanwen Yao, Weifeng Wang, Zhao Li, Ce Liang. Detection Method for Video Flame Super-Pixel Based on Optimized InceptionV1[J]. Laser & Optoelectronics Progress, 2021, 58(2): 0210004 Copy Citation Text show less
    Structure of Inception module
    Fig. 1. Structure of Inception module
    Structure of InceptionV1 module
    Fig. 2. Structure of InceptionV1 module
    Structure of improved Inception module
    Fig. 3. Structure of improved Inception module
    Front-end structure of improved InceptionV1 module
    Fig. 4. Front-end structure of improved InceptionV1 module
    Overall structure of improved InceptionV1 module
    Fig. 5. Overall structure of improved InceptionV1 module
    Feature extraction framework
    Fig. 6. Feature extraction framework
    Results of model parameter complexity optimization experiment
    Fig. 7. Results of model parameter complexity optimization experiment
    Final network structure
    Fig. 8. Final network structure
    Detection results of flame super-pixel. (a) Original image; (b) video super-pixel segmentation result; (c) full-frame detection result; (d) final detection result of removing non-flame area
    Fig. 9. Detection results of flame super-pixel. (a) Original image; (b) video super-pixel segmentation result; (c) full-frame detection result; (d) final detection result of removing non-flame area
    Ground truth annotation image
    Fig. 10. Ground truth annotation image
    Partial flame detection results of proposed method. (a) Image 1; (b) image 2; (c) image 3; (d) image 4; (e) image 5; (f) image 6
    Fig. 11. Partial flame detection results of proposed method. (a) Image 1; (b) image 2; (c) image 3; (d) image 4; (e) image 5; (f) image 6
    DatasetQuantity
    ImageNet dataset3067
    Bilkent University Fire dataset4782
    Durham University Fire dataset4563
    Non-fire dataset5439
    Table 1. Flame image data source and quantity
    Imp-AImp-BFocal-LossFLOPS/109Test accuracy /%
    1.50294.82
    1.50295.09
    1.23296.23
    1.23295.87
    1.46795.24
    1.46796.12
    1.19796.56
    1.19797.01
    Table 2. Results of ablation experiments
    MethodTPRFPRAPF1-score
    AlexNet0.910.190.880.920.91
    VGG-16[7]0.920.120.900.930.92
    InceptionV10.940.090.950.950.94
    Proposed method0.960.080.950.950.95
    Table 3. Comparison of index evaluation of different methods
    MethodC /106A /%ACFPS
    AlexNet72.091.71.2720.1
    VGG-16215.392.61.3613.8
    InceptionV16.195.215.6040.7
    Ref. [20]\\\14.2
    Ref. [19]\\\3.9
    Proposed method1.895.453.00108.4
    Table 4. Evaluation and comparison of network computing performance
    MethodTPRFPRF1-scorePA
    SEEDS+AlexNet0.860.290.840.830.86
    SEEDS+VGG160.850.310.820.800.85
    SEEDS+InceptionV10.940.180.920.910.94
    Ref. [20]0.910.130.880.860.87
    Ref. [19]0.870.170.880.900.90
    Proposed method0.940.040.940.950.96
    Table 5. Performance evaluation results of flame detection model
    MethodTPRF1-scorePS
    SEEDS+AlexNet0.800.780.760.78
    SEEDS+VGG160.850.840.840.76
    SEEDS+InceptionV10.900.880.870.87
    Proposed method0.910.900.890.90
    Table 6. Evaluation results of flame localization performance
    Jun Deng, Hanwen Yao, Weifeng Wang, Zhao Li, Ce Liang. Detection Method for Video Flame Super-Pixel Based on Optimized InceptionV1[J]. Laser & Optoelectronics Progress, 2021, 58(2): 0210004
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