• Infrared and Laser Engineering
  • Vol. 52, Issue 1, 20220344 (2023)
Xiangjun Wang1,2, Mingyang Li1,2, Lin Wang1,2, Feng Liu1,2, and Wei Wang1,2
Author Affiliations
  • 1State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China
  • 2MOEMS Education Ministry Key Laboratory, Tianjin University, Tianjin 300072, China
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    DOI: 10.3788/IRLA20220344 Cite this Article
    Xiangjun Wang, Mingyang Li, Lin Wang, Feng Liu, Wei Wang. Salient object detection method based on multi-scale feature-fusion guided by edge information[J]. Infrared and Laser Engineering, 2023, 52(1): 20220344 Copy Citation Text show less
    Structural diagram of channel fusion residual block (RCFBlock)
    Fig. 1. Structural diagram of channel fusion residual block (RCFBlock)
    Structural diagram of MCFUBlock
    Fig. 2. Structural diagram of MCFUBlock
    Structural diagram of expanded spatial attention module guided by edge information (EGSAM)
    Fig. 3. Structural diagram of expanded spatial attention module guided by edge information (EGSAM)
    Structure diagram of complete EGMFNet
    Fig. 4. Structure diagram of complete EGMFNet
    Structure diagram of U-block with residual connection
    Fig. 5. Structure diagram of U-block with residual connection
    EGMFNet prediction annotation rendering
    Fig. 6. EGMFNet prediction annotation rendering
    EGMFNet prediction annotation rendering
    Fig. 7. EGMFNet prediction annotation rendering
    ECSSDPASCAL-SHKU-ISDUTS-TE
    ${{F} }_{{\beta } }$$ {M}{A}{E} $${{S} }_{{\alpha } }$${{F} }_{{\beta } }$$ {M}{A}{E} $${{S} }_{{\alpha } }$${{F} }_{{\beta } }$$ {M}{A}{E} $${{S} }_{{\alpha } }$${{F} }_{{\beta } }$$ {M}{A}{E} $${{S} }_{{\alpha } }$
    MDF0.8320.1050.7760.7680.1460.6920.8610.1290.8100.7300.0940.792
    PiCaNet0.8860.0450.9170.8560.0780.8480.8700.0430.9040.7590.0510.869
    AFNet0.9080.0420.9130.8210.0700.8440.8880.0360.9050.7920.0460.867
    ${ {{\rm{R}}} }^{3}{{\rm{N}}}{{\rm{e}}}{{\rm{t}}}$0.9140.0400.9100.8450.0940.8000.8930.0360.8950.7850.0570.834
    PoolNet0.9150.0390.9210.8220.0740.8450.8920.0340.9110.8090.0400.883
    BPFINet0.9280.0340.9260.8450.0650.8570.9110.0280.9180.8380.0380.882
    Proposed0.9430.0330.9260.8680.0690.8560.9280.0330.9120.8520.0370.883
    Table 1. Comparison of the experimental results
    ${\rm{ Parameters}}$${\rm{Runtime}/s }$${\rm{Frame\;rate}/FPS }$
    R3Net 56 156 1260.03033
    PoolNet71 383 5770.03330
    BPFINet68 326 8530.03330
    Proposed60 638 9280.03132
    Table 2. Parameter quantity and real-time evaluation of EGFMNet
    GroupsStructureLossFβMAESα
    GABaselineBCE0.9230.0410.908
    GBBaselineBCE+BL0.9260.0400.911
    GCBaseline+EGSAM(3 stages)BCE0.9360.0360.919
    GDBaseline+EGSAM(3 stages)BCE+BL0.9430.0330.926
    Table 3. Ablation experimental results. GA is the basic network trained with BCE Loss, GB is the basic network trained with mixed loss, GC is the complete network with three-stage EGSAM module and trained with BCE Loss, and GD is the complete network with three-stage EGSAM module and trained with mixed loss
    No.FβMAESαParameters
    10.8890.0890.83164 609 584
    20.9430.0330.92660 638 928
    30.9250.0400.91148 277 712
    Table 4. RCFBlock stack quantity verification experiment
    No.Stage with EGSAMFβMAESαParametersSize/MB
    1Baseline0.9230.0410.90854 437 157207.67
    2Stage 10.9360.0380.91654 734 330208.79
    3Stage 1+20.9410.0350.92355 916 197213.30
    4Stage 1+2+30.9430.0330.92660 638 928231.32
    5Stage 1+2+3+40.9420.0330.92479 521 275303.35
    Table 5. Verify the experimental results at EGSAM module level
    No.αFβMAESα
    1Baseline0.9230.0410.908
    210.8420.1040.832
    30.10.9110.0520.894
    40.050.9260.0410.913
    50.010.9430.0330.926
    60.0050.9400.0370.920
    Table 6. EGSAM fusion coefficient setting experiment
    Xiangjun Wang, Mingyang Li, Lin Wang, Feng Liu, Wei Wang. Salient object detection method based on multi-scale feature-fusion guided by edge information[J]. Infrared and Laser Engineering, 2023, 52(1): 20220344
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