Acta Photonica Sinica, Volume. 50, Issue 2, 76(2021)

Research on Infrared Visible Image Fusion and Target Recognition Algorithm Based on Region of Interest Mask Convolution Neural Network

Yongping HAO1... Zhaorui CAO1,*, Fan BAI1, Haoyang SUN1, Xing WANG2 and Jie QIN1 |Show fewer author(s)
Author Affiliations
  • 1College of Equipment Engineering, Shenyang Ligong University, Shenyang059, China
  • 2College of Mechanical Engineering, Shenyang Ligong University, Shenyang110159, China
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    Figures & Tables(16)
    Network structure of IVFNN
    Feature extraction network structure of IVFNN
    Input image changes in the process of dual channel adaptive fusion and attention logic code calculation
    Comparison of imaging contribution of infrared and visible light targets in different environments
    Adaptive fusion process of infrared and visible feature maps group
    Feature maps of each channel before and after fusion
    The process of calculating the thermal radiation type type of image point
    Attention network in visible channel
    Feature maps of each channel before and after adding the logical mask of ROI
    Schematic diagram of CIOU in IVFNN
    Cross loss calculation of dual channels and fused images
    Loss function curve of IVFNN
    Recognition effect of IVFNN and each frequency band
    Recognition effect of low-heat radiation target
    • Table 1. Multi-scale target recognition ability of IVFNN and each frequency band

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      Table 1. Multi-scale target recognition ability of IVFNN and each frequency band

      Imaging frequency bandMinimum pixel of recognizableMinimum identifiable target pixel ratioLimit accuracy of minimum target recognition
      Infrared370×232 pixels6.98%65.3%
      Visible light452×292 pixels10.7%69.5%
      Dual channels adaptive fusion of IVFNN248×179 pixels3.61%60.7%
    • Table 2. Comparison of test results of each algorithm

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      Table 2. Comparison of test results of each algorithm

      Algorithm used

      Processes

      Average recognition rate

      Average calculation speed

      Ref.[2]

      Feature matching and fusion

      70.6%

      35 fps

      Ref.[3]

      Filtering fusion

      77.9%

      37 fps

      Ref.[6]

      Filtering fusion

      76.1%

      32 fps

      Ref.[7]

      Neural network fusion

      80.5%

      22 fps

      Ref.[10]

      Neural network fusion

      81.3%

      23 fps

      Proposed algorithm

      Global IVFNN

      83.2%

      25 fps

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    Yongping HAO, Zhaorui CAO, Fan BAI, Haoyang SUN, Xing WANG, Jie QIN. Research on Infrared Visible Image Fusion and Target Recognition Algorithm Based on Region of Interest Mask Convolution Neural Network[J]. Acta Photonica Sinica, 2021, 50(2): 76

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    Paper Information

    Category: Image Processing

    Received: --

    Accepted: --

    Published Online: Aug. 26, 2021

    The Author Email: CAO Zhaorui (caozhaorui@163.com)

    DOI:10.3788/gzxb20215002.0210002

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