• Laser & Optoelectronics Progress
  • Vol. 59, Issue 12, 1215005 (2022)
Gang Li1, Yongqiang Chen1、*, Tingquan He2, Yu Dai1, and Dongchao Lan1
Author Affiliations
  • 1School of Electronics and Control Engineering, Chang’an University, Xi’an 710064, Shaanxi , China
  • 2Information Department, Guangxi New Development Transportation Group Co., Ltd., Nanning 530029, Guangxi , China
  • show less
    DOI: 10.3788/LOP202259.1215005 Cite this Article Set citation alerts
    Gang Li, Yongqiang Chen, Tingquan He, Yu Dai, Dongchao Lan. Crack Detection Algorithm Based on Improved Multibranch Feature Shared Structure Network[J]. Laser & Optoelectronics Progress, 2022, 59(12): 1215005 Copy Citation Text show less
    Depth separable convolution. (a) Convolution process; (b) convolution structure
    Fig. 1. Depth separable convolution. (a) Convolution process; (b) convolution structure
    Pyramid pooling module
    Fig. 2. Pyramid pooling module
    Improved multi-branch feature shared structure network
    Fig. 3. Improved multi-branch feature shared structure network
    Structure diagram of modules. (a) GCN module; (b) BR module
    Fig. 4. Structure diagram of modules. (a) GCN module; (b) BR module
    Structure diagram of RRC module
    Fig. 5. Structure diagram of RRC module
    Process of dataset annotation and crop
    Fig. 6. Process of dataset annotation and crop
    Training loss and accuracy obtained by changing the initial learning rate. (a) Loss; (b) accuracy
    Fig. 7. Training loss and accuracy obtained by changing the initial learning rate. (a) Loss; (b) accuracy
    Visual comparison results of different algorithms in crack images with noise
    Fig. 8. Visual comparison results of different algorithms in crack images with noise
    Change trend of each indicator. (a) Evaluation index; (b) PR curve
    Fig. 9. Change trend of each indicator. (a) Evaluation index; (b) PR curve
    Segmentation effects of the proposed algorithm on the public dataset
    Fig. 10. Segmentation effects of the proposed algorithm on the public dataset
    Schematic of visualized results of crack skeleton extraction
    Fig. 11. Schematic of visualized results of crack skeleton extraction
    Comparison of measurement errors of crack length and width
    Fig. 12. Comparison of measurement errors of crack length and width
    ParameterTraining setValidation setTest set
    Image size /(pixel×pixel)512×512512×512512×512
    Number of images /frame1200040004000
    Table 1. Partition of crack dataset
    AlgorithmPrecisionRecallF1MIoUFPS(Millisecond/image)
    Fast-SCNN0.75480.79620.77490.769267.5(14.81)
    SegNet0.83120.78640.80820.820618.7(53.48)
    Pspnet0.90210.91150.90680.802129.3(34.13)
    DeepCrack0.84630.81590.83080.864225.6(39.06)
    CrackU-Net0.87360.79380.83180.852734.8(28.74)
    Proposed algorithm0.96950.92830.94850.890259.4(16.84)
    Table 2. Performance comparison of different crack detection algorithms
    DatasetPrecisionRecallF1MIoUFPS(Millisecond/image)
    Cracktree2000.94320.95090.94700.877254.5(18.35)
    GAPs3840.95270.94450.94860.849553.8(18.59)
    Crack5000.96830.94850.95830.867956.2(17.79)
    Table 3. Test results of the proposed algorithm on different public datasets
    ParameterFast-SCNNSegNetPspnetDeepCrackCrackU-NetProposed algorithm
    Relative error of length /%8.627.286.965.735.144.73
    Relative error of width /%7.946.537.326.876.025.21
    Table 4. Relative errors of length and width of different comparison algorithms
    Gang Li, Yongqiang Chen, Tingquan He, Yu Dai, Dongchao Lan. Crack Detection Algorithm Based on Improved Multibranch Feature Shared Structure Network[J]. Laser & Optoelectronics Progress, 2022, 59(12): 1215005
    Download Citation