Robust 3D object detection method based on localization uncertainty

doi:10.11772/j.issn.1001-9081.2020122055

Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (10): 2979-2984.DOI: 10.11772/j.issn.1001-9081.2020122055

Special Issue: 多媒体计算与计算机仿真

• Multimedia computing and computer simulation • Previous Articles Next Articles

Robust 3D object detection method based on localization uncertainty

PEI Yiyao^1,2, GUO Huiming², ZHANG Danpu², CHEN Wenbo³

1. The 2nd Academy of China Aerospace Science and Industry Corporation, Beijing 100039, China;
2. Beijing Aerospace Changfeng Science Technology Industry Group Company Limited, Beijing Aerospace Changfeng Company Limited, Beijing 100039, China;
3. Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China

Received:2020-12-29 Revised:2021-02-24 Online:2021-10-10 Published:2021-10-27
Supported by:
This work is partially supported by the National Key Research and Development Program of China.

基于定位不确定性的鲁棒3D目标检测方法

裴仪瑶^1,2, 郭会明², 张丹普², 陈文博³

1. 中国航天科工集团第二研究院, 北京 100039;
2. 北京航天长峰股份有限公司北京航天长峰科技工业集团有限公司, 北京 100039;
3. 中国科学院自动化研究所, 北京 100190

通讯作者: 裴仪瑶
作者简介:裴仪瑶(1996-),女,山西太原人,硕士研究生,主要研究方向:深度学习、图像处理;郭会明(1965-),男,湖北天门人,研究员,硕士,主要研究方向:深度学习、图像处理;张丹普(1986-),女,河南平顶山人,高级工程师,博士,主要研究方向:深度学习、图像处理;陈文博(1993-),男,河北沧州人,工程师,硕士,主要研究方向:深度学习、三维感知与建模。
基金资助:
国家重点研发计划项目。

Abstract

Abstract: To solve the problem of inaccurate localization of model which is caused by inaccurate manual labeling in 3D point cloud training data, a novel robust 3D object detection method based on localization uncertainty was proposed. Firstly, with the 3D voxel grid-based Sparsely Embedded CONvolutional Detection (SECOND) network as basic network, the prediction of localization uncertainty was added based on Region Proposal Network (RPN). Then, during the training process, the localization uncertainty was modeled by using Gaussian and Laplace distribution models, and the localization loss function was redefined. Finally, during the prediction process, the threshold filtering and Non-Maximum Suppression (NMS) methods were performed to filter candidate objects based on the object confidence which was consisted of the localization uncertainty and classification confidence. Experimental results on the KITTI 3D object detection dataset show that compared with SECOND network, the proposed algorithm has the detection accuracy improved by 0.5 percentage points on car category at moderate level. The detection accuracy of the proposed algorithm is 3.1 percentage points higher than that of SECOND network with adding disturbance simulation noise to the training data in the best case. The proposed algorithm improves the accuracy of 3D object detection, which reduces false detection and improves the accuracy of 3D bounding boxes, and is more robust to noisy data.

Key words: point cloud, 3D object detection, uncertainty, Convolutional Neural Network (CNN), robustness, Sparsely Embedded CONvolutional Detection (SECOND) network

摘要： 针对3D点云训练数据因人工标注不精确而导致模型定位不准确的问题，提出了一种基于定位不确定性的鲁棒3D目标检测方法。首先，以基于3D体素网格的稀疏嵌入卷积检测（SECOND）网络作为基础网络，在候选区域生成网络（RPN）的基础上增加对定位不确定性的预测；然后，在训练过程中使用高斯和拉普拉斯两种分布模型对定位不确定性进行建模，并对定位损失函数进行重新定义；最后，在预测过程中结合定位不确定性和分类置信度作为目标置信度，使用阈值过滤和非极大值抑制（NMS）方法筛选候选目标。实验结果表明，在KITTI 3D目标检测数据集上，所提算法对于车辆类别的检测准确率在中等难度上比SECOND网络提高了0.5个百分点；当在训练数据中额外加入扰动模拟噪声的情况下，所提算法的检测准确率比SECOND网络最多提高了3.1个百分点。所提算法提高了3D目标检测准确率，减少了误检且提高了3D边界框的精度，并且对于带噪声的数据更鲁棒。

关键词: 点云, 3D目标检测, 不确定性, 卷积神经网络, 鲁棒性, 稀疏嵌入卷积检测网络

CLC Number:

TP391.41

PEI Yiyao, GUO Huiming, ZHANG Danpu, CHEN Wenbo. Robust 3D object detection method based on localization uncertainty[J]. Journal of Computer Applications, 2021, 41(10): 2979-2984.

裴仪瑶, 郭会明, 张丹普, 陈文博. 基于定位不确定性的鲁棒3D目标检测方法[J]. 计算机应用, 2021, 41(10): 2979-2984.

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Robust 3D object detection method based on localization uncertainty

基于定位不确定性的鲁棒3D目标检测方法

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