摘要
为了提高道路检测的精度,提出了一种基于改进全卷积神经网络(FCN)的路侧激光雷达可行驶空间检测方法。首先,通过建立环形栅格图并统计栅格内的点云信息,生成二维俯视图;然后,在FCN中引入混合膨胀卷积以替换标准卷积,并在池化层后添加空间特征对齐模块,在卷积层后添加通道特征对齐模块,从而构建HCS-FCN。在自主构建的16线路侧激光雷达道路数据集和32线路侧激光雷达道路数据集上,将HCS-FCN与传统的FCN和SegNet进行对比实验。实验结果显示,HCS-FCN的F_(1)分数在16线数据集和32线数据集上分别达到88.4%和89.2%;平均像素准确率在16线数据集和32线数据集上分别达到89.1%和89.7%;平均像素交并比在16线数据集和32线数据集上分别达到87.3%和88.9%;均优于传统FCN和SegNet。
In order to improve the accuracy of road detection,a roadside lidar drivable space detection method based on an improved fully convolutional neural network(FCN)was proposed.Firstly,a two-dimensional top view is generated by building a ring grid map and counting the point cloud information in the grid.Then,a hybrid dilated convolution is introduced in FCN to replace the standard convolution,and a spatial feature alignment module is added after the pooling layer,and a channel feature alignment module is added after the convolution layer to construct HCS-FCN.HCS-FCN is compared with traditional FCN and SegNet on the self-constructed 16-line roadside lidar road dataset and 32-line roadside lidar road dataset.The experimental results show that the F_(1)-score of HCS-FCN reaches 88.4%and 89.2%on the 16-line dataset and 32-line dataset respectively;the average pixel accuracy reaches 89.1%and 89.7%on the 16-line dataset and 32-line dataset respectively;the average pixel intersection over union ratio reaches 87.3%and 88.9%on the 16-line dataset and 32-line dataset respectively;all of which are better than traditional FCN and SegNet.
作者
朱进玉
杨若楠
鲍宇健
张凯
唐尔迪
汪贵平
ZHU Jinyu;YANG Ruonan;BAO Yujian;ZHANG Kai;TANG Erdi;WANG Guiping(School of Electrical and Control Engineering,Chang’an University,Xi’an 710064,China)
出处
《激光杂志》
北大核心
2025年第1期97-105,共9页
Laser Journal
基金
国家自然科学基金(No.71901040)
陕西省重点研发计划项目(No.2023-JC-YB-507)
陕西省重点研发计划项目(No.2019GY-083)
咸阳市秦创原科技创新项目(No.2021ZDZX-GY-0009)
咸阳市重点研发计划项目(No.L2022-ZDYF-GY-004)。
作者简介
朱进玉(1991-),男,硕士,工程师。研究方向:交通信息工程及控制。E-mail:jyzhu@chd.edu.cn;通讯作者:汪贵平(1960-),男,博士,二级教授。研究方向:车路协同。E-mail:gpwang@chd.edu.cn。
