北京大学学报（自然科学版）2026，Vol.62Issue(5)：986-998,13.DOI:10.13209/j.0479-8023.2026.068
FE-PointRCNN:基于特征增强 PointRCNN 的自动驾驶三维目标检测方法
FE-PointRCNN:3D Object Detection for Autonomous Driving Based on Feature-Enhanced PointRCNN
摘要
Abstract
To address the challenges in autonomous driving environment perception—namely,that pure vision-based schemes are prone to misjudgment,existing 3D point cloud object detection algorithms suffer from insufficient feature representation due to the sparsity and inhomogeneity of point clouds,low detection accuracy in complex scenarios,and the fact that most algorithms only validate single-category objects,failing to meet the multi-category perception requirements of national standards—a feature-enhanced PointRCNN network(FE-PointRCNN)is proposed.To overcome the limitations of traditional MLP structures,which possess strong global feature extraction capabilities but weak local geometric feature capture,along with the high computational overhead of attention mechanisms,Enhanced Multi-Layer Perceptrons(E-MLPs)and Residual Channel Attention(RC-Attention)modules are designed.These modules are integrated to construct an improved E-PointNet++feature extraction architecture that enables parallel multi-scale feature extraction and adaptive weighted enhancement of key channel features,thereby alleviating the gradient vanishing problem and enhancing feature discriminability.Comparative and ablation experiments are conducted on the KITTI and ONCE datasets.The results demonstrate that the proposed FE-PointRCNN achieves accuracy improvements over the baseline PointRCNN* for the Car,Pedestrian,and Cyclist categories and exhibits strong robustness on the complex-scene ONCE dataset.The proposed algorithm effectively improves point cloud feature extraction capability and multi-category object detection performance,providing valuable technical support for LiDAR-based perception systems in autonomous driving.关键词
三维目标检测/自动驾驶/强化多层感知机/残差通道注意力Key words
3D object detection/autonomous driving/enhanced multi-layer perceptron/residual channel attention引用本文复制引用
张宝文,赵永永,苏成志,曹国华..FE-PointRCNN:基于特征增强 PointRCNN 的自动驾驶三维目标检测方法[J].北京大学学报（自然科学版）,2026,62(5):986-998,13.基金项目
太原科技大学博士科研启动基金(20262020)和山西省来晋优秀博士科研项目(20262091)资助 （20262020）
