• Laser & Optoelectronics Progress
  • Vol. 59, Issue 12, 1215006 (2022)
Kai Yang1, Rui Li1、*, Lin Luo1, and Liming Xie2
Author Affiliations
  • 1School of Physical Science and Technology, Southwest Jiaotong University, Chengdu 610031, Sichuan , China
  • 2Chengdu Leading Technology Co., Ltd., Chengdu 610073, Sichuan , China
  • show less
    DOI: 10.3788/LOP202259.1215006 Cite this Article Set citation alerts
    Kai Yang, Rui Li, Lin Luo, Liming Xie. Research on Train Key Components Detection Based on Improved RetinaNet[J]. Laser & Optoelectronics Progress, 2022, 59(12): 1215006 Copy Citation Text show less
    RetinaNet network structure
    Fig. 1. RetinaNet network structure
    Bottleneck structure
    Fig. 2. Bottleneck structure
    P6 and P7 layers structure
    Fig. 3. P6 and P7 layers structure
    RFB module
    Fig. 4. RFB module
    PAN module
    Fig. 5. PAN module
    Validation results of two networks on PASCAL VOC dataset. (a) mAP; (b) loss curves
    Fig. 6. Validation results of two networks on PASCAL VOC dataset. (a) mAP; (b) loss curves
    Detection results of RetinaNet
    Fig. 7. Detection results of RetinaNet
    Detection results of improved method
    Fig. 8. Detection results of improved method
    NameConfiguration
    CPUIntel i5-8300H
    GPUGTX1060,6GB
    SystemWindows10
    Cuda/cuDNN9.0/9.0
    FrameworkPython3/Tensorflow1.4
    Table 1. Computing environment configuration
    MethodmAPAeroBikeBirdBoatBottleBusCarCatChairCow
    RetinaNet0.7700.880.840.800.670.500.810.850.930.560.83
    Proposed0.7760.890.840.810.660.520.810.860.940.570.85
    MethodTableDogHorseMbikePersonPlantSheepSofaTrainTV
    RetinaNet0.640.900.830.840.810.520.770.740.890.79
    Proposed0.660.900.850.830.820.520.770.730.890.79
    Table 2. Validation results on PASCAL VOC dataset (AP)
    CategoryTraining setTesting set
    Objectsm-s ratio /%Objectsm-s ratio /%
    Brake5749123392
    U-lock194949989
    Cotter pin2358810496
    Bolt454273143664
    Hexagonal lock164988797
    Nameplate3396313081
    Table 3. Key component dataset
    P3、P4、P5、P6、P7P3、P4、P5mAPWeight /MB
    0.952116
    0.951109
    Table 4. Influence of different feature layers on detection effect
    ModelmAPBrakeU-lockCotter pinBoltHexagonal lockNameplate
    RetinaNet0.9510.940.980.930.950.970.94
    RetinaNet+RFB0.9580.950.990.950.950.960.95
    Proposed0.9640.950.990.960.950.970.96
    Table 5. AP comparison between proposed method and RetinaNet
    MethodmAPInference time /s
    YOLOV30.850.08
    YOLOV40.930.10
    FasterRCNN0.920.25
    FCOS0.890.10
    SSD0.930.10
    RetinaNet0.950.11
    Proposed0.960.12
    Table 6. Comparison of detection speed and efficiency of different models
    Kai Yang, Rui Li, Lin Luo, Liming Xie. Research on Train Key Components Detection Based on Improved RetinaNet[J]. Laser & Optoelectronics Progress, 2022, 59(12): 1215006
    Download Citation