To solve the problems that the existing single-stage 3D object detection algorithm utilizes point cloud down-sampling features in a single way and the degree of aggregation of features for the long-range contextual information cannot meet the requirement of enhancing the algorithm performance,we propose a single-stage 3D object detection al-gorithm based on multi-channel cross attention fusion.First,the channel-wise cross attention module is designed to fuse the down sampled features,which can enhance the expression ability of multi-scale features for the long-range spatial information under different receptive field based on the cross attention mechanism.Then,a cascade feature excitation module is proposed to combine the original downsampling features to cascade channel-wise cross attention weighted features to enhance the algorithm's learning ability for key spatial features.Extensive experiments were conducted on the public autonomous driving dataset KITTI and compared with mainstream algorithms.As a single-stage algorithm,the detection accuracy was 91.34%,79.85%and 75.98%for the three difficulty levels of car categories,which were 4.83%,3.26%and 3.32%better than the baseline algorithm.The experimental results demonstrate the effectiveness and ad-vancement of the algorithm and the proposed modules for 3D object detection task.
关键词
三维点云/自动驾驶/激光雷达/深度学习/三维目标检测/柱体素/交叉注意力/单阶段算法
Key words
3D point cloud/autonomous driving/LiDAR/deep learning/3D object detection/pillar/cross attention/single-stage algorithm
维普
万方数据