• Opto-Electronic Engineering
  • Vol. 51, Issue 6, 240066-1 (2024)
Zhenjiu Xiao, Jiehao Zhang*, and Bohan Lin
Author Affiliations
  • School of Software, Liaoning University of Engineering and Technology, Huludao, Liaoning 125105, China
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    DOI: 10.12086/oee.2024.240066 Cite this Article
    Zhenjiu Xiao, Jiehao Zhang, Bohan Lin. Feature coordination and fine-grained perception of small targets in remote sensing images[J]. Opto-Electronic Engineering, 2024, 51(6): 240066-1 Copy Citation Text show less
    Overall model structure
    Fig. 1. Overall model structure
    Structure of KernelWarehouse
    Fig. 2. Structure of KernelWarehouse
    Feature synergy module
    Fig. 3. Feature synergy module
    Fine-grained aware detect head
    Fig. 4. Fine-grained aware detect head
    Aware attention mechanism
    Fig. 5. Aware attention mechanism
    Training results of the YOLOv8 algorithm
    Fig. 6. Training results of the YOLOv8 algorithm
    Training results of the proposed algorithm
    Fig. 7. Training results of the proposed algorithm
    Comparison of detection results
    Fig. 8. Comparison of detection results
    NumberABCDPrecision/%Recall/%FPSmAP@0.5 /%mAP@0.5:.0.95 /%
    1××××79.068.743474.350.9
    2×××82.170.238476.552.5
    3×××84.469.037075.251.6
    4×××80.169.747675.551.4
    5×××80.570.445475.851.8
    6××83.372.356678.053.9
    7××84.969.928576.452.8
    8×84.873.840279.855.6
    9×82.371.741677.053.0
    1084.375.645481.758.0
    Table 1. Ablation experiments of the proposed algorithm in the DOTA1.0 dataset
    CategoryYOLOv5YOLOv7CornerNetR-FCNYOLO-BiFPNYOLO-PWCAYOLO-DCTIOurs
    SV75.176.510.149.881.777.686.888.4
    LV86.786.750.245.185.485.790.990.3
    PL93.692.364.781.191.892.691.993.0
    ST74.370.957.967.477.972.785.777.5
    SH89.689.131.349.389.187.581.191.7
    HA87.783.580.545.288.284.188.589.8
    GTF71.155.524.958.969.364.273.675.4
    SBF62.158.422.741.866.864.570.670.7
    TC94.094.985.568.993.494.093.896.0
    SP82.879.518.553.364.164.283.085.0
    BD76.171.138.258.974.078.777.378.8
    RA64.347.144.551.459.462.063.965.2
    BC78.372.262.552.176.275.582.182.5
    BR59.245.326.231.656.451.857.157.7
    HC84.481.712.133.983.076.385.287.2
    mAP@0.578.673.642.052.680.278.880.681.7
    Table 2. Experimental results of different algorithms on DOTA1.0 dataset; unit: %
    CategoryYOLOv5YOLOv7CornerNetR-FCNYOLO-BiFPNYOLO-PWCAYOLO-DCTIOurs
    SV57.866.550.359.770.571.275.277.3
    LV71.482.159.658.977.886.388.589.6
    PL80.588.476.577.384.190.780.190.1
    ST77.880.968.170.576.273.175.777.6
    SH76.785.360.764.877.186.487.389.4
    HA82.681.777.875.186.980.689.190.5
    GTF73.780.664.160.377.575.374.976.1
    SBF63.268.458.361.673.466.875.175.2
    TC85.583.280.779.887.689.583.791.0
    SP76.178.672.973.460.570.180.481.3
    BD79.378.268.670.680.183.784.884.9
    RA73.475.470.266.574.668.770.176.9
    BC78.381.173.474.880.482.680.783.1
    BR60.362.563.266.367.169.862.670.2
    HC68.865.662.760.178.977.482.480.3
    CC62.767.864.566.973.170.374.275.6
    mAP@0.576.375.672.070.878.777.978.180.4
    Table 3. Experimental results of different algorithms on DOTA1.5 dataset; unit: %
    Zhenjiu Xiao, Jiehao Zhang, Bohan Lin. Feature coordination and fine-grained perception of small targets in remote sensing images[J]. Opto-Electronic Engineering, 2024, 51(6): 240066-1
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