计算机科学 ›› 2022, Vol. 49 ›› Issue (11A): 210900079-6.doi: 10.11896/jsjkx.210900079

• 图像处理&多媒体技术 • 上一篇    下一篇

基于点云数据的交通环境下单阶段三维目标检测方法

车爱博1, 张辉2, 李晨1, 王耀南2   

  1. 1 长沙理工大学电气与信息工程学院 长沙 410114
    2 湖南大学机器人学院 长沙 410082
  • 出版日期:2022-11-10 发布日期:2022-11-21
  • 通讯作者: 张辉(zhanghuihby@126.com)
  • 作者简介:(392765762@qq.com)

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.

摘要: 文中在CIA-SSD单阶段三维目标检测模型的基础上,将模型中空间语义特征融合方式进行改进,通过一种基于注意力机制的多通道融合模块对两特征进行融合,提出了单阶段检测方法TFAF-SSD(Two-Feature Attentional Fusion Single-Stage object Detector),该方法主要由流形稀疏卷积网络提取点云的稀疏特征后,再由空间语义卷积层分别提取检测对象的空间语义特征,对融合后的输出特征进行预测,最后通过检测头输出最终的检测框。同时,文中还运用了不同于以往方法的数据增强方法,增强了模型的泛化性能,达到了提升检测精度的效果。在KITTI 3D公开数据集上进行了验证,在测试集中汽车检测方面得到了中等检测难度AP值为83.77%的检测结果,相比CIA-SSD模型的80.28%,所提方法提升了3.49%。

关键词: 点云数据, 三维目标检测, 稀疏卷积, 特征融合, 数据增强, KITTI数据集

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

中图分类号: 

  • TP183
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