Volume 58 Issue 24
Dec 2022
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JOURNAL OF MECHANICAL ENGINEERING  > 2022  >  58(24): 289-299.
LIU Yonggang, YU Fengning, ZHANG Xinjie, CHEN Zheng, QIN Datong. Research on 3D Object Detection Based on Laser Point Cloud and Image Fusion[J]. JOURNAL OF MECHANICAL ENGINEERING, 2022, 58(24): 289-299. doi: 10.3901/JME.2022.24.289
Citation: LIU Yonggang, YU Fengning, ZHANG Xinjie, CHEN Zheng, QIN Datong. Research on 3D Object Detection Based on Laser Point Cloud and Image Fusion[J]. JOURNAL OF MECHANICAL ENGINEERING, 2022, 58(24): 289-299. doi: 10.3901/JME.2022.24.289
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Research on 3D Object Detection Based on Laser Point Cloud and Image Fusion

doi: 10.3901/JME.2022.24.289
  • 1.

    State Key Laboratory of Mechanical Transmissions, College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044

  • 2.

    State Key Laboratory of Automotive Simulation and Control, Jinlin University, Changchun 130025

  • 3.

    Faculty of Transportation Engineering in Kunming University of Science and Technology, Kunming 650500

  • Received Date: 19 Jan 2022
  • Rev Recd Date: 26 Sep 2022
    • Available Online: 07 Mar 2024
  • Issue Publish Date: 20 Dec 2022
    Abstract
  • At present, 3D object detection based on the fusion of lidar and camera has received extensive attention. However, most fusion algorithms are difficult to accurately detect small target objects such as pedestrians and cyclists. Therefore, a feature fusion network based on the self-attention mechanism is proposed, which fully considers the local feature information to achieve accurate 3D object detection. Firstly, to reduce the spatial search range of the point cloud, the Faster-RCNN is improved to form a candidate box. Then, the frustum point cloud was extracted according to the projection relationship between the lidar and the camera. Secondly, a Self-Attention PointNet based on the self-attention mechanism is proposed to segment the original point cloud data within the scope of the frustum. Finally, while using the PointNet and T-Net to predict the 3D bounding box parameters, the regularization term is considered in the loss function to achieve higher convergence accuracy. The KITTI dataset is used for verification and testing. The results show that this method is obviously superior to F-PointNet and the detection accuracy of cars, pedestrians, and cyclists has been greatly improved, and it has higher accuracy than mainstream 3D object detection networks.

     

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