Autonomous Driving 3D Object Detection Based on Cascade YOLOv7

被引：0
|
作者：
Zhao D. [1 ]
Zhao S. [1 ]
机构：
[1] School of Mechatronics and Vehicle Engineering, Chongqing Jiaotong University, Chongqing
来源：
Qiche Gongcheng/Automotive Engineering | 2023年 / 45卷 / 07期
关键词：
3D object detection; autonomous driving; F-PointNet; multi-sensor information fusion; YOLOv7;
D O I：
10.19562/j.chinasae.qcgc.2023.07.002
中图分类号：
学科分类号：
摘要：
For the problems of incomplete feature information and excessive point cloud search volume in 3D object detection methods based on image and original point cloud，based on Frustum PointNet structure，a 3D object detection algorithm based on cascade YOLOv7 is proposed by fusing RGB image and point cloud data of autonomous driving surrounding scenes. Firstly，a frustum estimation model based on YOLOv7 is constructed to longitudinally expand the RGB image RoI into 3D space. Then the object point cloud and background point cloud in the frustum are segmented by PointNet ++. Finally，the natural position relationship between objects is explained by using the non-modal 3D box estimation network to output the length，width，height，heading et al. of objects. The test results and ablation experiments on the KITTI public dataset show that compared with the benchmark network，the inference time of cascade YOLOv7 model is shortened by 40 ms∙frame-1，with the mean average precision of detection at the moderate，difficulty level increased by 8.77%，9.81%，respectively. © 2023 SAE-China. All rights reserved.
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页码：1112 / 1122
页数：10
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