Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (11A): 210900079-6.doi: 10.11896/jsjkx.210900079

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Single-stage 3D Object Detector in Traffic Environment Based on Point Cloud Data

CHE Ai-bo1, ZHANG Hui2, LI Chen1, WANG Yao-nan2   

  1. 1 School of Electrical and Information Engineering,Changsha University of Science and Technology,Changsha 410114,China
    2 School of Robotics,Hunan University,Changsha 410082,China
  • Online:2022-11-10 Published:2022-11-21
  • About author:CHE Ai-bo,born in 1995,master.Her main research interests include machine vision and deep lear-ning.
    ZHANG Hui,born in 1983,Ph.D.His main research interests include intelligent robot vision detection,deep learning image recognition,and robot intelligent control.

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Abstract: Based on CIA-SSD single-stage 3D object detection model,this paper improves the spatial semantic feature fusion method of the model.A multi-channel fusion module based on attention mechanism is used to fuse the two features.The single-stage detection method TFAF-SSD(two-feature attentional fusion single-stage object detector) is proposed.After extracting the sparse features of point cloud by manifold sparse convolution network,the spatial semantic features of the detected objects are extracted by the spatial semantic convolution layer.After fusion,the output features are predicted.Finally,the final detection frame is output by the detection head.At the same time,different from the previous methods,this paper also uses the data enhancement method to enhance the generalization performance of the model to improve the detection accuracy.The proposed method is verified on KITTI 3D open data set,and the test result of vehicle detection in test set is 83.77%,which is 3.49 % higher than the 80.28% of CIA-SSD model.

Key words: Point cloud data, 3D object detection, Sparse convolution, Feature fusion, Data enhancement, KITTI dataset

CLC Number: 

  • TP183
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