Laser & Optoelectronics Progress, Volume. 61, Issue 18, 1812006(2024)

3D Object Detection Algorithm Based on Improved YOLOv5

Xueqing Sheng, Shaobin Li*, Jinyan Qu, and Liu Liu
Author Affiliations
  • School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
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    Figures & Tables(12)
    Detection flow of YOLOv5
    Network structure of 3D target detection method based on improved YOLOv5 algorithm
    Overall network framework structure of Complex-YOLO algorithm
    Rotating target box labeling. (a) Definition of angle of rotation; (b) schematic of angle coding
    Structure of small target detection layer
    Visualization of feature map. (a) RGB-Map; (b) feature map of small target layer; (c) feature map of shallow layer; (d) feature map of middle layer; (e) feature map of deep layer
    CBAM Attention Module Structure
    AP and detection speed comparison between different algorithms on KITTI dataset
    Comparison of detection performance between Complex-YOLO, PointPillars algorithm, and proposed algorithm
    Visualization results of proposed method
    • Table 1. AP comparison of different algorithms on KITTI dataset

      View table

      Table 1. AP comparison of different algorithms on KITTI dataset

      MethodDetection speed /(frame/s)AP of Car-BEV /%AP of Pedestrian-BEV /%AP of Cyclist-BEV /%
      EasyModerateHardEasyModerateHardEasyModerateHard
      MV3D2.886.0276.9068.49N/AN/AN/AN/AN/AN/A
      VoxelNet4.389.3579.2677.3946.1340.4738.1166.7054.7650.55
      F-PointNet5.988.7084.0075.3358.0950.2247.2075.3861.9654.68
      AVOD12.586.8085.4477.7342.5135.2433.9763.6647.7446.55
      AVOD-FPN10.088.5383.7977.9050.6644.7540.8362.3952.0247.87
      SECOND20.088.0779.3777.9555.1046.2744.7673.6756.0448.78
      PointPillars62.088.3586.1079.8358.6650.2347.1979.1462.2556.00
      DVFENet20.090.9387.6884.6050.9844.1241.6282.2967.4060.71
      PointRGBNet12.591.3985.7380.6838.0729.3226.9473.0957.5951.78
      Complex-YOLO50.485.8977.4077.3346.0845.9044.2072.3763.3660.27
      Proposed30.492.2387.8982.9563.6956.6853.4270.8067.5161.28
    • Table 2. Ablation experimental results of proposed algorithm on KITTI validation set

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      Table 2. Ablation experimental results of proposed algorithm on KITTI validation set

      MethodSmall targets layerCBAMNumber of parameters /106FLOPs /109Car mAP /%Pedestrian mAP /%

      Cyclist

      mAP /%

      All class mAP /%Detection speed /(frame/s)
      Complex-YOLO80.2145.3965.3363.6450.4
      YOLOv57.5017.390.5741.6569.6667.2973.0
      YOLOv5+ small targets layer8.4933.485.4146.2161.6064.4036.4
      YOLOv5+CBAM7.5417.486.4746.7865.2266.1657.8
      Proposed8.4232.887.6957.9366.5370.7230.4
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    Xueqing Sheng, Shaobin Li, Jinyan Qu, Liu Liu. 3D Object Detection Algorithm Based on Improved YOLOv5[J]. Laser & Optoelectronics Progress, 2024, 61(18): 1812006

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    Paper Information

    Category: Instrumentation, Measurement and Metrology

    Received: Jan. 5, 2024

    Accepted: Feb. 21, 2024

    Published Online: Sep. 14, 2024

    The Author Email: Shaobin Li (shbli@bjtu.edu.cn)

    DOI:10.3788/LOP240451

    CSTR:32186.14.LOP240451

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