Laser & Optoelectronics Progress, Volume. 61, Issue 22, 2215007(2024)

Task Feature Decoupling Model for Autonomous Driving Visual Joint Perception

Yue Wang* and Jiale Cao
Author Affiliations
  • School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
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    Figures & Tables(10)
    Architecture of task feature decoupling model for autonomous driving visual joint perception
    The feature encoding method of HSEM (n=2)
    The feature encoding method of SIRM (m=0)
    Task feature decoupling network for dual-branch semantic segmentation decoder
    The visualized results of TFDJP,the multi-task model YOLOP,the single-task models PSPNet,YOLOv5, and SCNN on the BDD100K dataset. (a) The visualized results of YOLOP; (b) the visualized results of TFDJP; (c) the visualized results of PSPNet, YOLOv5, and SCNN
    • Table 1. Ablation experiment results of the proposed module in object detection decoder of TFDJP model

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      Table 1. Ablation experiment results of the proposed module in object detection decoder of TFDJP model

      MethodHSEMSIRMIoU perception prediction branchRecall /%mAP50 /%
      A89.276.5
      B90.378.4
      C90.678.3
      D91.178.8
      E91.779.2
    • Table 2. Ablation experiment results of different module in drivable area segmentation and lane detection decoder

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      Table 2. Ablation experiment results of different module in drivable area segmentation and lane detection decoder

      MethodHRBLRBFFM

      Edge

      loss

      mIoU /%Acc /%IoU /%
      A91.570.526.2
      B92.679.928.3
      C92.679.628.4
      D92.780.728.3
      E92.881.128.5
    • Table 3. Experiment results of object detection

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      Table 3. Experiment results of object detection

      ModelRecall /%mAP50 /%Frame rate /(frame/s)
      Faster R-CNN2477.255.69.7
      YOLOv5s586.877.2150.4
      PP-YOLOE2582.359.947.0
      MultiNet*1581.360.215.7
      DLT-Net*1689.468.417.1
      AMTNet*1988.377.868.8
      YOLOP*1789.276.576.3
      TFDJP*91.779.267.5
    • Table 4. Experiment results of drivable area segmentation

      View table

      Table 4. Experiment results of drivable area segmentation

      ModelmIoU /%Frame rate /(frame/s)
      PSPNet1089.69.7
      MultiNet*1571.615.7
      DLT-Net*1671.317.1
      AMTNet*1988.368.8
      YOLOP*1791.576.3
      TFDJP*92.867.5
    • Table 5. Experiment results of lane detection

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      Table 5. Experiment results of lane detection

      ModelAcc /%IoU /%
      ENet2634.1214.64
      SCNN1135.7915.84
      ENet-SAD27]36.5616.02
      AMTNet*1973.6026.91
      YOLOP*1770.5026.20
      TFDJP*81.1028.50
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    Yue Wang, Jiale Cao. Task Feature Decoupling Model for Autonomous Driving Visual Joint Perception[J]. Laser & Optoelectronics Progress, 2024, 61(22): 2215007

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    Paper Information

    Category: Machine Vision

    Received: Jan. 19, 2024

    Accepted: Apr. 11, 2024

    Published Online: Nov. 19, 2024

    The Author Email: Yue Wang (2021234212@tju.edu.cn)

    DOI:10.3788/LOP240559

    CSTR:32186.14.LOP240559

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