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Acta Optica Sinica, Volume. 42, Issue 9, 0915001(2022)

Polarization Imaging Detection of Individual Camouflage Based on Two-Stream Fusion Network

Rongchang Wang1,2, Feng Wang1,2、*, Shuaijun Ren1,2, and Yong Wang1,2
Author Affiliations
  • 1Department of Information Engineering, PLA Army Artillery Air Defense Force College, Hefei 230031, Anhui, China
  • 2Key Laboratory of Polarized Light Imaging Detection Technology of Anhui Province, Hefei 230031, Anhui, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (22)
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    References (19)
    Cited By (5)
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    Figures & Tables(22)
    Schematic of light propagation

    Fig. 1. Schematic of light propagation

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    Layout diagram of color focal plane polarized pixel array

    Fig. 2. Layout diagram of color focal plane polarized pixel array

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    Structure diagram of TSF-Net

    Fig. 3. Structure diagram of TSF-Net

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    Structure diagram of ANN

    Fig. 4. Structure diagram of ANN

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    Structure diagram of APP-Net

    Fig. 5. Structure diagram of APP-Net

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    Structure diagram of APP-Net

    Fig. 6. Structure diagram of APP-Net

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    Structure diagram of RGB-Net

    Fig. 7. Structure diagram of RGB-Net

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    Process of feature extraction and feature fusion

    Fig. 8. Process of feature extraction and feature fusion

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    Schematic of training and test process

    Fig. 9. Schematic of training and test process

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    Physical drawing of portable acquisition equipment

    Fig. 10. Physical drawing of portable acquisition equipment

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    Schematic of classification of individual camouflage polarization image dataset

    Fig. 11. Schematic of classification of individual camouflage polarization image dataset

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    Two types of camouflage target test diagram. (a) Multicam type camouflage; (b) Woodland type camouflage

    Fig. 12. Two types of camouflage target test diagram. (a) Multicam type camouflage; (b) Woodland type camouflage

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    Detection effects of different models in Multicam dataset. (a) SSD model; (b) YOLOv4 model; (c) YOLOv5 model; (d) RetinaNet model; (e) Faster R-CNN model; (f) TSF-Net model

    Fig. 13. Detection effects of different models in Multicam dataset. (a) SSD model; (b) YOLOv4 model; (c) YOLOv5 model; (d) RetinaNet model; (e) Faster R-CNN model; (f) TSF-Net model

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    Detection effects of different models in Woodland dataset. (a) SSD model; (b) YOLOv4 model; (c) YOLOv5 model; (d) RetinaNet model; (e) Faster R-CNN model; (f) TSF-Net model

    Fig. 14. Detection effects of different models in Woodland dataset. (a) SSD model; (b) YOLOv4 model; (c) YOLOv5 model; (d) RetinaNet model; (e) Faster R-CNN model; (f) TSF-Net model

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    Parameter verification result

    Fig. 15. Parameter verification result

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    Verification results for different branches. (a) Detection accuracy of different v values; (b) IOU-mAP curves

    Fig. 16. Verification results for different branches. (a) Detection accuracy of different v values; (b) IOU-mAP curves

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    • Table 1. Training results with different structures

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      Table 1. Training results with different structures

      StructureGPU memoryusage /MBTime/minLoss
      (8,16,8,3)14532251.23×10-2
      (16,8,8,3)14532169.51×10-3
      (96,48,32,3)38172077.48×10-3
      (48,96,32,3)38172295.79×10-3
      (128,96,64,32,3)61092555.55×10-3
      (96,128,64,32,3)61092824.27×10-3

    • Table 2. Parameters of color focal plane camera

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      Table 2. Parameters of color focal plane camera

      CategoryParameter
      Camera modelFLIR BFS-U3-51S5PC-C
      Resolution /(pixel×pixel)2448×2048
      Frame rate /(frame·s-1)75
      Chip modelSony IMX250MYR,Polar-RGB
      Data interfaceUSB3.1 Gen1
      Size and weight /(mm×mm×mm)29×29×30
      Mass /g36
      Lens interfaceC-Mount

    • Table 3. Positive and negative cases

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      Table 3. Positive and negative cases

      CasePrediction (positive)Prediction (negative)
      Ture(true)TPTN
      Ture(false)FPFN

    • Table 4. Comparison of detection accuracy of different models

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      Table 4. Comparison of detection accuracy of different models

      ModelmAP /%
      Multicam datesetWoodland dataset
      SSD70.973.5
      YOLOv471.573.4
      YOLOv573.174.6
      RetinaNet75.277.5
      Faster R-CNN77.178.9
      TSF-Net85.987.1

    • Table 5. Comparison of posture test results of different camouflage personnel

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      Table 5. Comparison of posture test results of different camouflage personnel

      ModelmAP /%
      SFSSSB
      TSF-Net84.984.384.6

    • Table 6. Cross-validation comparison

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      Table 6. Cross-validation comparison

      ModelmAP /%
      M/WW/M
      Faster R-CNN23.515.7
      TSF-Net48.835.5
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    Rongchang Wang, Feng Wang, Shuaijun Ren, Yong Wang. Polarization Imaging Detection of Individual Camouflage Based on Two-Stream Fusion Network[J]. Acta Optica Sinica, 2022, 42(9): 0915001

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    Paper Information

    Category: Machine Vision

    Received: Sep. 3, 2021

    Accepted: Nov. 17, 2021

    Published Online: May. 21, 2022

    The Author Email: Wang Feng (wfissky7202@sina.com)

    DOI:10.3788/AOS202242.0915001

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology