Chinese Journal of Liquid Crystals and Displays, Volume. 38, Issue 9, 1281(2023)

Obstacle detection method for guide system based on CE-YOLOX

Yuan LIU, Rong-fen ZHANG, Yu-hong LIU*, Na-na CHENG, Xin-fei LIU, and Shuang YANG
Author Affiliations
  • College of Big Data and Information Engineering,Guizhou University,Guiyang 550025,China
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    Figures & Tables(20)
    YOLOX network structure
    Structure of CE-YOLOX network
    Sub-pixel skip fusion(SSF)module
    Illustration of sub-pixel context enhancement(SCE)
    Illustration of channel attention guided module(CAG)
    Overview of GAM
    Channel attention submodule
    Spatial attention submodule
    Scheme for calculation of angle cost contribution to the loss function
    Scheme for calculation of the distance between the ground truth bounding box and the prediction of it
    Comparison of YOLOX and CE-YOLOX test results
    Comparison of three model checks
    Flow diagram of guide system
    • Table 1. Number of each type of test in the dataset

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      Table 1. Number of each type of test in the dataset

      CategoryNumberCategoryNumber
      Bicyc858Car1 544
      Cat789Bench1 815
      Dog729Fire-hydrant1 175
      Motorbike604Banma757
      Person3 444Garbagecan1 105
      Pottedplant2 128Stairs1 395
      Isolation-pile1 108Pothole2 127
      Road-cone2 696Puddle1 044
      Boulder-ball2 567Step1 392
      Stopper1 992Obstacle1 371
    • Table 2. Experimental conditions

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      Table 2. Experimental conditions

      CategoryParameter
      CPUAMD 3900X
      GPUNvidia GTX 3090
      Operating systemUbuntu 16.04
      Development softwarePyCharm
      Development frameworkPytorch
    • Table 3. Experimental parameter setting

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      Table 3. Experimental parameter setting

      参数名参数值
      Input_Size[640,640]
      Init_Lr0.01
      Min_Lr0.000 1
      Optimizer_TypeSGD
      Momentum0.937
      Lr_Decay_TypeCOS
    • Table 4. Results of ablation experiment

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      Table 4. Results of ablation experiment

      GroupsMethodsmAP/%FPSModel_size/MB
      0YOLOX88.0890.8654.209
      1+SIOU88.2285.5954.209
      2+SIOU+CE-PAFPN90.0278.1363.514
      3+VariFocalLOSS88.3583.5354.209
      4+VariFocalLOSS+CE-PAFPN89.6477.1063.514
      5+VariFocalLOSS+CE-PAFPN+GAM90.4173.7893.43
      6+SIOU+CE-PAFPN+GAM(Ours)90.5375.9393.43
    • Table 5. Comparison of the effects of different position GAM

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      Table 5. Comparison of the effects of different position GAM

      GAM添加位置mAP/%
      CE-PAFPN之前89.08
      CE-PAFPN之后90.53
    • Table 6. Comparison of different algorithms

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      Table 6. Comparison of different algorithms

      ModelsmAP/%FPS
      RetineNet81.7146.23
      Efficiented80.9633.18
      SSD82.3649.45
      YOLOv579.3599.79
      YOLOX88.0890.86
      CE-YOLOX(Ours)90.5375.93
    • Table 7. Comparison of detection effects of the three algorithms on Nvidia Xavir NX

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      Table 7. Comparison of detection effects of the three algorithms on Nvidia Xavir NX

      ModelmAP/%FPS
      YOLOv579.3531.54
      YOLOX88.0829.89
      CE-YOLOX(Ours)90.5325.78
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    Yuan LIU, Rong-fen ZHANG, Yu-hong LIU, Na-na CHENG, Xin-fei LIU, Shuang YANG. Obstacle detection method for guide system based on CE-YOLOX[J]. Chinese Journal of Liquid Crystals and Displays, 2023, 38(9): 1281

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    Paper Information

    Category: Research Articles

    Received: Oct. 26, 2022

    Accepted: --

    Published Online: Sep. 19, 2023

    The Author Email: Yu-hong LIU (liuyuhongxy@sina.com)

    DOI:10.37188/CJLCD.2022-0358

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