智能系统学报2024，Vol.19Issue(2)：259-266,8.DOI:10.11992/tis.202301001
远距离和遮挡下三维目标检测算法研究
Long-distance and occluded 3D target detection algorithm
摘要
Abstract
To address the limitations of existing 3D target detection algorithms,particularly their poor detection per-formance for occluded and long-distance objects,we have implemented an enhancement to the PointRCNN network,a 3D object detection algorithm based on point cloud.We began by voxelizing the region of interest obtained from the re-gion proposal network and constructing region pyramids of different scales to capture a wider range of points of interest.Simultaneously,we introduced a point cloud transformer module to enhance the learning of the local features of grid center points.Moreover,we incorporated a sphere query radius prediction module into the network.This addition al-lows the model to adaptively adjust the sphere query range according to the density of the point cloud.Finally,the ef-fectiveness of the proposed algorithm was validated through rigorous experimental testing.We evaluated the perform-ance of the model using the KITTI data set and designed corresponding ablation experiments to verify the effectiveness of each module in the model.关键词
目标检测/深度学习/激光雷达点云/远距离目标/遮挡下目标/自动驾驶/区域金字塔/特征提取Key words
target detection/deep learning/Lidar point cloud/long-distance target/occluded target/autopilot/regional pyramid/feature extraction分类
计算机与自动化引用本文复制引用
陆军,李杨,鲁林超..远距离和遮挡下三维目标检测算法研究[J].智能系统学报,2024,19(2):259-266,8.基金项目
国家自然科学基金项目(52171332) （52171332）
黑龙江省自然科学基金项目(F201123). （F201123）
