Chinese Journal of Liquid Crystals and Displays, Volume. 38, Issue 12, 1707(2023)

Attention and cross-scale fusion for vehicle and pedestrian detection

Jian-dong LI1,2, Jia-qi LI1、*, and Hai-cheng QU1
Author Affiliations
  • 1College of Software,Liaoning Technical University,Huludao 125105,China
  • 2College of Mining,Liaoning Technical University,Fuxin 123000,China
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    Figures & Tables(14)
    Network structure of SSD
    Network structure of AF-SSD
    Forward propagation path of foreign information
    Network structure of SRFPN
    Self-adaptive feature fusion module
    Criss-cross attention module
    Detection results comparion of SSD and proposed algorithm in the sub-samples of the PASCAL VOC
    Detection results comparion of SSD and proposed algorithm on RTP target data set
    • Table 1. Data distribution for RTP

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      Table 1. Data distribution for RTP

      数据来源目标个数
      CarPerson
      总计104 78527 216
      KITTI103 45324 560
      网络图像7081 650
      真实街道拍摄6241 006
    • Table 2. Results of the ablation experiment

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      Table 2. Results of the ablation experiment

      SRBPNSAFMCCNmAP/%FPS
      84.085.0
      85.580.6
      86.279.0
      87.177.0
    • Table 3. Comparison of detection accuracy of all types in the sub-samples of the PASCAL VOC dataset

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      Table 3. Comparison of detection accuracy of all types in the sub-samples of the PASCAL VOC dataset

      ModelAPmAP
      CarBusBikeMotorPerson
      ION30087.588.686.283.682.185.6
      Faster R-CNN84.783.179.077.576.780.2
      YOLOv33387.787.685.586.283.386.1
      DC-SPP-YOLO3483.987.684.985.777.083.8
      FCOS88.184.886.884.884.285.7
      LNFCOS87.385.285.683.983.485.1
      SSD30085.787.083.984.079.484.0
      DSSD32186.285.684.986.779.784.6
      RSSD3586.386.185.984.080.284.5
      EDF-SSD87.387.087.187.381.085.9
      AFP-SSD3686.688.085.485.979.485.1
      RLCADet3786.087.086.786.479.185.0
      AFE-SSD3887.489.085.986.685.987.0
      DF-SSD3986.385.485.086.279.184.4
      Proposed88.287.487.987.584.487.1
    • Table 4. Comparison of verification results on Titan X for different algorithms

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      Table 4. Comparison of verification results on Titan X for different algorithms

      ModelNetworkmAP/%FPS
      Faster R-CNNVGG1680.27.0
      ION300VGG1685.6-
      R-FCN40ResNet-10184.19.0
      DSSD321ResNet-10184.69.5
      RSSDVGG1684.535.0
      SSDVGG1684.046.0
      ProposedVGG1687.141.5
    • Table 5. Comparison of verification results on 1080Ti for different algorithms

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      Table 5. Comparison of verification results on 1080Ti for different algorithms

      ModelNetworkmAP/%FPS
      YOLOv3Darknet-5386.139.0
      RLCADetResnet10185.036.0
      SSDVGG1684.085.0
      ProposedVGG1687.177.0
    • Table 6. Comparison of detection results on RTP

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      Table 6. Comparison of detection results on RTP

      ModelNetworkmAP/%FPS
      SSDVGG1673.285.0
      ProposedVGG1677.177.0
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    Jian-dong LI, Jia-qi LI, Hai-cheng QU. Attention and cross-scale fusion for vehicle and pedestrian detection[J]. Chinese Journal of Liquid Crystals and Displays, 2023, 38(12): 1707

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    Paper Information

    Category: Research Articles

    Received: Feb. 6, 2023

    Accepted: --

    Published Online: Mar. 7, 2024

    The Author Email: Jia-qi LI (dor_emma@163.com)

    DOI:10.37188/CJLCD.2023-0037

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