点云场景下基于结构感知的车辆检测  

Vehicle Detection Based on Structure Perception in Point Cloud

作　　者：李宗民[1,2] 姚纯纯 刘玉杰 李华[3,4] Li Zongmin;Yao Chunchun;Liu Yujie;Li Hua(College of Computer Science and Technology,China University of Petroleum,Qingdao 266580;Shengli College of China University of Petroleum,Dongying 257061;Key Laboratory of Intelligent Information Processing,Institute of Computing Technology,Chinese Academy of Sciences,Beijing 100190;University of Chinese Academy of Sciences,Beijing 100049)

机构地区：[1]中国石油大学(华东)计算机科学与技术学院,青岛266580 [2]中国石油大学胜利学院,东营257061 [3]中国科学院计算技术研究所智能信息处理重点实验室,北京100190 [4]中国科学院大学,北京100049

出　　处：《计算机辅助设计与图形学学报》2021年第3期405-412,共8页Journal of Computer-Aided Design & Computer Graphics

基　　金：国家自然科学基金(61379106,61379082,61227802);山东省自然科学基金(ZR2013FM036,ZR2015FM011);中央高校基本科研业务费专项资金(18CX06050A).

摘　　要：在自动驾驶领域,计算机对周围环境的感知和理解是必不可少的.其中,相比于二维目标检测,三维点云目标检测可以提供二维目标检测所不具有的物体的三维方位信息,这对于安全自动驾驶是至关重要的.针对三维目标检测中原始输入点云到检测结果之间跨度大的问题,首先,提出了基于结构感知的候选区域生成模块,其中定义了每个点的结构特征,充分利用了三维点云目标检测数据集提供的监督信息,通过预测该特征,网络可以学习到更具有鉴别能力的特征,从而提高候选框的生成质量;其次,将该特征加入到候选框微调阶段中,使得点云上下文特征和局部特征更加丰富.在三维点云目标检测数据集进行了实验,结果表明,文中方法能够在增加极少计算量的前提下,在候选区域生成阶段使用50个候选框0.7的IoU阈值下,提高超过13%的召回率;在候选框微调阶段,3种难度目标框的检测效果均有明显提升,表明了该方法对三维点云目标检测的有效性.In the field of automatic driving,computer perception and understanding of the surrounding environment is essential.Compared with 2D object detection,3D point cloud object detection can provide the three-dimensional information of the object that the 2D object detection does not have.In order to solve the problem of large disparity between the original input point cloud and the detection result in 3D object detection,a region proposal generation module based on structure awareness is proposed,in which the structural features of each point are defined,and the supervision information provided by the 3D point cloud object detection dataset is fully utilized.The network can learn more discriminative features to improve the quality of proposals.Secondly,the feature is added to the proposal fine-tuning stage to enrich the context features and local features of point cloud.Evaluated on KITTI 3D object detection dataset,in the region proposal generation stage,under the IoU threshold of 0.7,using 50 proposals,there is a more than 13%increase in the recall rate compared to previous results.In the proposal fine-tuning stage,the detection results of the 3 difficulty levels objects is obviously improved,indicating the effectiveness of the proposed method for 3D point cloud object detection.

关 键 词：三维点云目标检测 结构特征 候选区域生成网络 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]