Journal of Infrared and Millimeter Waves, Volume. 43, Issue 4, 582(2024)

Research on fast detection method of infrared small targets under resource-constrained conditions

Rui ZHANG1, Min LIU1, and Zheng LI2、*
Author Affiliations
  • 1School of Opto-Electronic and Comunication Engineering,Xiamen University of Technology,Xiamen 361024,China
  • 2Shanghai Institute of Technical Physics,Chinese Academy of Sciences,Shanghai 200083,China
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    Figures & Tables(12)
    Infrared small UAVs
    Improved YOLOv5s network architecture
    Small target detection head
    The sensitivity analysis of IoU on infrared small UAV
    Dataset analysis:(a) the label category distribution; (b) the bounding box size distribution; (c) the label center position distribution; (d) the label size distribution
    Performance comparison of the AP:(a) AP@0.5; (b) AP@0.5:0.95
    Some examples of the detection result on the improved model
    Framework of the deployment
    • Table 1. Comparison of the different weighting coefficient results

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      Table 1. Comparison of the different weighting coefficient results

      Weighting Coefficients

      AP@0.5

      (%)

      AP@0.5:0.95

      (%)

      FAR

      (%)

      MR

      (%)

      LP2+LP3+LP4+0.LP581.244.96.428.4
      LP2+LP3+LP4+0.LP586.847.64.719.4
      LP2+LP3+LP4+0.LP584.246.07.822.8
      LP2+LP3+LP4+0.LP588.448.64.017.4
    • Table 2. Comparison of ablation experiments of improved methods

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      Table 2. Comparison of ablation experiments of improved methods

      ModelsAP@0.5(%)

      AP@0.5:0.95

      (%)

      FAR

      (%)

      MR

      (%)

      YOLOv5s84.746.13.123.6
      YOLOv5s+0.5NWD87.447.94.017.4
      YOLOv5s+NWD89.948.14.414.6
      YOLOv5s+P288.448.64.017.4
      YOLOv5s+NWD+P291.950.04.212.9
    • Table 3. Comparison of improved YOLOv5s with other methods

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      Table 3. Comparison of improved YOLOv5s with other methods

      Models

      AP@0.5

      (%)

      AP@0.5:0.95

      (%)

      FAR

      (%)

      MR

      (%)

      Parameter

      (M)

      GFLOPs

      Speed

      (FPS)

      Weights

      (MB)

      SSD-ResNet5060.422.829.746.213.115.0200105.1
      Faster-RCNN-ResNet5078.330.921.240.041.1134.550330.3
      RetinaNet-ResNet5082.133.818.533.232.0127.543257.3
      YOLOv383.345.28.722.49.323.152618.9
      YOLOv5s84.746.13.123.67.015.862514.4
      YOLOv5m86.648.83.120.420.847.930342.2
      YOLOv5l87.749.13.817.646.1107.619692.8
      YOLOv8s89.548.96.317.311.128.443522.5
      YOLOv5s+NWD+P291.950.04.212.97.726.840016.3
    • Table 4. Result of the deployment

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      Table 4. Result of the deployment

      BM1684XFPSAP@0.5 (%)AP@0.5:0.95(%)
      FP321291.950.0
      FP169587.849.8
      INT8163--
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    Rui ZHANG, Min LIU, Zheng LI. Research on fast detection method of infrared small targets under resource-constrained conditions[J]. Journal of Infrared and Millimeter Waves, 2024, 43(4): 582

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    Paper Information

    Category: Research Articles

    Received: Nov. 6, 2023

    Accepted: --

    Published Online: Aug. 27, 2024

    The Author Email: Zheng LI (lizheng_sitp@163.com)

    DOI:10.11972/j.issn.1001-9014.2024.04.019

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