软件导刊2025，Vol.24Issue(3)：170-176,7.DOI:10.11907/rjdk.241954
基于改进VirConv算法的点云三维目标检测
Point Cloud 3D Object Detection Based on Improved VirConv Algorithm
摘要
Abstract
To address the challenges of high false detection rates and poor performance in detecting distant and small objects in current point cloud 3D object detection algorithms,this paper proposes an improved 3D object detection method based on the VirConv algorithm.We design a Bird's Eye View(BEV)feature extraction network called C-ECVNet,which optimizes the point cloud encoding network of VirConv.This network introduces the ECVBlock module to enhance object features,enabling more precise extraction of spatial structure information from the original point cloud.Additionally,it incorporates a channel self-attention mechanism to capture hierarchical attention between channels,thereby improving model efficiency and generalization ability,while enhancing feature extraction capabilities.Experimental results on the KIT-TI test set demonstrate that our algorithm exhibits stronger robustness and lower false detection rates when processing complex environments,distant targets,and small objects.关键词
点云/三维目标检测/VirConv/特征增强Key words
point cloud/3D object detection/VirConv/feature enhancement分类
信息技术与安全科学引用本文复制引用
梁凯,徐义春,董方敏,孙水发..基于改进VirConv算法的点云三维目标检测[J].软件导刊,2025,24(3):170-176,7.基金项目
国家自然科学基金项目(61871258) （61871258）
