Laser & Optoelectronics Progress, Volume. 58, Issue 4, 0410023(2021)

TDFF: Strong Robust Algorithm for Smoke Image Detection

Weigang Wang, Bingwei Wang*, and Yunwei Zhang
Author Affiliations
  • School of Electronic and Optical Engineering, School of Microelectronics, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu 210023, China
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    Figures & Tables(10)
    Different types of features in heterogeneous modes. (a1)--(a4) Linear features; (b1)--(b4) T-type features; (c1)(c2) cross and diagonal features
    Processing flow of TDFF algorithm
    Extraction results of different features. (a1)(a2) Traditional LBP features; (b1)(b2) T-MFLBP features
    Comparison of detection rates of different methods in different datasets
    Comparison of false alarm rates of different methods in different datasets
    Detection rate comparison curves of different methods in different dimensions
    Comparison curves of false alarm rates in different dimensions by different methods
    Comparison curves of detection rates of different feature fusion methods under different number of iterations
    Comparison curves of false alarm rates of different feature fusion methods under different number of iterations
    • Table 1. Performance comparison of different feature fusion methods unit: %

      View table

      Table 1. Performance comparison of different feature fusion methods unit: %

      DatasetLBP+GaborT-MFLBP+GaborTDFF
      Detection rateFalse alarm rateDetection rateFalse alarm rateDetection rateFalse alarm rate
      194109461004
      297139611998
      39779941002
      49313929996
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    Weigang Wang, Bingwei Wang, Yunwei Zhang. TDFF: Strong Robust Algorithm for Smoke Image Detection[J]. Laser & Optoelectronics Progress, 2021, 58(4): 0410023

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    Paper Information

    Category: Image Processing

    Received: Jul. 10, 2020

    Accepted: Sep. 3, 2020

    Published Online: Feb. 24, 2021

    The Author Email: Wang Bingwei (1113627530@qq.com)

    DOI:10.3788/LOP202158.0410023

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