基于N3D_DIOU的图像与点云融合目标检测算法

Object detection algorithm based on image and point cloud fusion with N3D_DIOU

目标检测是自主驾驶和机器人导航的基础,针对二维图像信息量不足,三维点云数据量大、密度不均匀和检测精度低等问题,本文基于深度学习提出了一种融合二维图像与三维点云的目标检测网络进行三维目标检测。为减少运算量,论文首先用二维图像检测器生成的检测框对应的平截头体对原始点云进行滤波;为解决点云密度不均匀问题,提出了一种基于广义霍夫变换的改进投票模型网络用于多尺度特征提取;最后将二维DIOU(Distance Intersection over Union)损失函数扩展为三维空间的N3D_DIOU(Normal 3 Dimensional DIOU)损失函数,提高了生成框和目标框的一致性,进一步提高了点云检测精度。在KITTI数据集上进行的大量实验表明:与经典方法相比,本文算法在汽车三维检测精度上提升了0.71%,在鸟瞰图检测精度上提升了7.28%,取得了较好效果。

Object detection is the basis of autonomous driving and robot navigation.To solve the problems of insufficient information in 2D images and the large data volume,uneven density,and low detection accuracy of 3D point clouds,a new 3D object-detection network is proposed through an image and point-cloud fusion with deep learning.To reduce the calculation load,the original point cloud is first filtered with the flat interceptor corresponding to the object's frame detected in the 2D image.To address the uneven density,an improved voting model network,based on a generalized Hough transform,is proposed for multiscale feature extraction.Finally,Normal Three-Dimensional Distance Intersection over Union(N3D_DIOU),a novel loss function,is extended from the Two-Dimensional Distance Intersection over Union(2D DIOU)loss function,which improves the consistency between the generated and target frames,and also improves the object-detection accuracy of the point cloud.Experiments on the KITTI dataset show that our algorithm improves the accuracy of three-dimensional detection by 0.71%,and the aerial-view detection accuracy by 7.28%,over outstanding classical methods.