Opto-Electronic Engineering, Volume. 51, Issue 12, 240210-1(2024)

A road extraction algorithm that fuses element multiplication and detail optimization

Jin Zhang1、*, Minghai Lv1,2, Yongan Feng1, and Ying Zhang3
Author Affiliations
  • 1School of Software, Liaoning Technical University, Huludao, Liaoning 125105, China
  • 2Teaching Supervision Office, Liaoning Technical University, Huludao, Liaoning 125105, China
  • 3China Eye Hospital, China Academy of Chinese Medical Sciences, Beijing 100040, China
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    Figures & Tables(17)
    Road extraction algorithm incorporating elemental multiplication and detail optimisation
    IEM block
    Comparison of receptive fields in different models. (a) IEM block sensory field; (b) IEM block without two Conv3×3 branches
    Depthwise convolution
    Pointwise convolution
    Classification of 2D planes
    Three-dimensional plane classification
    Comparison of heat maps. (a) Image; (b) Conv3×3; (c) Original IEM; (d) IEM
    Parameter-free attention mechanism of PFAAM
    Comparison of heat maps. (a) Image; (b) Direct output; (c) Output + RRN
    Output loss function value conversion curves
    Comparison of visualization results extracted by various methods
    Comparison of visualization results extracted by various methods
    • Table 1. Experimental results of ablation on the Massachusetts road dataset

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      Table 1. Experimental results of ablation on the Massachusetts road dataset

      No.Base lineBoth of Conv3×3IEMMaxpoolRRNOA/%mIoU/%IoU/%F1/%
      N197.7880.0762.4187.88
      N297.7980.1562.5987.91
      N397.9480.8363.8488.28
      N497.9680.9163.9788.34
      N598.0681.2564.5288.70
    • Table 2. Experimental results of the IEMUnet algorithm layer ablation

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      Table 2. Experimental results of the IEMUnet algorithm layer ablation

      No.IoU/%mIoU/%Params/M
      N163.4080.7631.24
      N264.5281.2551.47
      N364.5481.2582.15
    • Table 3. Comparison of road extraction results from different networks

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      Table 3. Comparison of road extraction results from different networks

      ModelOA/%mIoU/%IoU/%F1/%FLOPs/MParams/MLatency/s
      Deeplabv3+97.7279.8161.7287.5826433.375.813.59
      A2FPN97.7479.8361.9587.6741832.9422.823.81
      ABCNet97.7379.7761.8387.6215761.6113.433.87
      U-Net97.7880.0762.4187.8825836.0124.893.76
      SegNet97.8080.3162.8487.82160675.429.443.88
      DLinkNet5097.8880.3962.7987.88120312.56217.654.72
      MANet97.8280.0862.4387.8677455.0965.863.89
      DSCNet97.9080.1562.4887.9021757.0747.435.35
      IEMUnet98.0681.2564.5288.7055094.1251.473.67
    • Table 4. Comparison of road extraction results from different networks

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      Table 4. Comparison of road extraction results from different networks

      ModelOA/%mIoU/%IoU/%F1/%
      Deeplabv3+96.9577.4758.1285.95
      A2FPN97.0378.0159.1386.37
      ABCNet96.9176.9757.1685.55
      U-Net97.0677.5858.2186.02
      SegNet97.0678.0359.1586.40
      DLinkNet5097.0077.6258.3886.06
      MANet97.0277.9659.0486.33
      DSCNet96.6376.3656.2485.10
      IEMUnet97.1978.6660.2486.85
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    Jin Zhang, Minghai Lv, Yongan Feng, Ying Zhang. A road extraction algorithm that fuses element multiplication and detail optimization[J]. Opto-Electronic Engineering, 2024, 51(12): 240210-1

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    Paper Information

    Category: Article

    Received: Sep. 2, 2024

    Accepted: Nov. 19, 2024

    Published Online: Feb. 21, 2025

    The Author Email:

    DOI:10.12086/oee.2024.240210

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