• Laser & Optoelectronics Progress
  • Vol. 57, Issue 18, 181015 (2020)
Yi Zhang, Zhiyuan Gong*, and Wenwen Wei
Author Affiliations
  • School of Communication and Information Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
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    DOI: 10.3788/LOP57.181015 Cite this Article Set citation alerts
    Yi Zhang, Zhiyuan Gong, Wenwen Wei. Traffic Sign Detection Based on Improved Faster R-CNN Model[J]. Laser & Optoelectronics Progress, 2020, 57(18): 181015 Copy Citation Text show less
    Framework of Faster R-CNN
    Fig. 1. Framework of Faster R-CNN
    Block of ResNeXt
    Fig. 2. Block of ResNeXt
    Block of improved basic network
    Fig. 3. Block of improved basic network
    Basic network structure of multi-dimensional feature fusion
    Fig. 4. Basic network structure of multi-dimensional feature fusion
    RPN structure
    Fig. 5. RPN structure
    Specific parameters of anchor frame type
    Fig. 6. Specific parameters of anchor frame type
    Samples of TT100K data set. (a) image 1; (b) image 2; (c) image 3; (d) image 4
    Fig. 7. Samples of TT100K data set. (a) image 1; (b) image 2; (c) image 3; (d) image 4
    Training set attributes. (a) Number of samples in training set; (b) images corresponding to some traffic sign numbers
    Fig. 8. Training set attributes. (a) Number of samples in training set; (b) images corresponding to some traffic sign numbers
    Loss function curves
    Fig. 9. Loss function curves
    P-R curves. (a) Improved basic network; (b) improved basic network+multi-scale feature fusion; (c) improved basic network+multi-scale feature fusion+improved anchor generation
    Fig. 10. P-R curves. (a) Improved basic network; (b) improved basic network+multi-scale feature fusion; (c) improved basic network+multi-scale feature fusion+improved anchor generation
    Example of detection effect of proposed algorithm. (a) Complex background detection; (b) detection of small targets
    Fig. 11. Example of detection effect of proposed algorithm. (a) Complex background detection; (b) detection of small targets
    StageOutput sizeNetwork structure
    Conv11024×1024Convolution kernel size: 7×7, 64, stride: 2;max pooling size: 3×3, 64, stride: 2
    Conv2512×5121×1,1281×1,3×3,5×5,7×7,128,1,C=4×81×1,256×3
    Conv3256×2561×1,2561×1,3×3,5×5,7×7,256,1,C=4×81×1,512×3
    Conv4128×1281×1,5121×1,3×3,5×5,7×7,512,1,C=4×81×1,1024×3
    Conv564×641×1,10241×1,3×3,5×5,7×7,1024,1,C=4×81×1,2048×3
    Table 1. Basic network parameters
    NameConfiguration
    CPUIntel Core i7 8700K
    GPUNVIDIA RTX 2080Ti
    RAM/G32
    Hard disk /TB2
    Table 2. Hardware configuration parameters
    AlgorithmAP /%Time /s
    Traditional Faster R-CNN80.280.33
    Ref.[19]81.130.31
    Ref.[8]91.675.81
    Proposed algorithm90.832.87
    Table 3. Performance comparison of detection algorithms
    Yi Zhang, Zhiyuan Gong, Wenwen Wei. Traffic Sign Detection Based on Improved Faster R-CNN Model[J]. Laser & Optoelectronics Progress, 2020, 57(18): 181015
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