目标检测
水准点(测量)
点云
计算机科学
人工智能
特征提取
计算机视觉
特征(语言学)
棱锥(几何)
方向(向量空间)
对象(语法)
相似性(几何)
激光雷达
模式识别(心理学)
集合(抽象数据类型)
视觉对象识别的认知神经科学
职位(财务)
图像(数学)
数学
遥感
地理
几何学
财务
大地测量学
程序设计语言
经济
语言学
哲学
作者
Qian Zhang,Huizheng Che,Jun Li,Ruijun Liu
标识
DOI:10.1109/iciba56860.2023.10165202
摘要
Aiming at the problems that point cloud has few available features and low positioning accuracy of small object in 3D object detection process. A new 3D object detection algorithm CFPointPillars based on improved PointPillars is proposed. First, before the feature extraction of the pseudo-image, Coordinate attention(CA) is introduced to obtain the position perception and direction perception information of the small object. Secondly, Feature Pyramid Network (FPN) structure is introduced to integrate the extracted features to obtain the precise semantic information of the small object. Finally test on KITTI public data set; The experimental results show that in terms of BEV detection benchmark, 3D detection benchmark and Average orientation Similarity(AOS), the mAP of CFPointPillars detection algorithm for car, pedestrians and cyclists reaches 70.79%, 64.44% and 71.75% respectively, which is 1.93%, 0.87% and 2.29% improvement compared with original network PointPillars respectively.
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