Research on 3D target detection algorithm based on PointPillars
To better identify long-distance and occluded targets in autonomous driving scenarios,this paper improves the PointPillars algorithm.Parallel spatial attention and channel attention mechanisms are introduced to enhance the target's position information and useful feature channel weights and thus improve the detection accuracy of long-distance targets.An adaptive spatial feature fusion module is employed in the 2D CNN backbone network,which addresses information loss in feature splicing and improves the detection accuracy of occluded targets.Based on the KITTI dataset,this paper conducts quantitative analysis and verification on SECOND,PointPillars,and our improved PointPillars algorithm under three different scenarioes.Our improved PointPillars algorithm is also visualized.Results show our method achieves a maximum improvement of 2.75％in target detection accuracy in bird's-eye view mode,of 2.93％in three-dimensional mode,and of 4.05％in AOS mode.Our visualization results indicate the improved PointPillars algorithm effectively detects long-distance and occluded targets.
国家科技期刊平台
NSTL
万方数据
