Multi-object Detection Algorithm Based on Point Cloud for Autonomous Driving Scenarios

被引：0
作者：
Tao, Le [1 ]
Wang, Hai [1 ]
Cai, Yingfeng [2 ]
Chen, Long [2 ]
机构：
[1] School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang
[2] Automotive Engineering Research Institute, Jiangsu University, Zhenjiang
来源：
Qiche Gongcheng/Automotive Engineering | 2024年 / 46卷 / 07期
关键词：
anchor-free; autonomous driving; deep learning; point cloud detection;
D O I：
10.19562/j.chinasae.qcgc.2024.07.008
中图分类号：
学科分类号：
摘要：
The three-dimensional object detection algorithm based on point cloud is one of the key technologies in the autonomous driving system. Currently，the voxel-based anchor-free detection algorithm is a research hotspot in academia，but most researches focus on designing complex refinement stage，at the expense of huge algorithm latency，to bring limited performance improvement. Although the single-stage anchor-free point cloud detection algorithm has a more streamlined detection process，its detection performance cannot satisfy the needs of autonomous driving scenarios. In this regard，based on the anchor-free detection algorithm CenterPoint，a single-stage anchor-free point cloud object detection algorithm for autonomous driving scenarios is proposed in this paper. Specifically，the encoding and decoding sparse module is introduced in this paper，which greatly promotes the information interaction of the spatial non-connected areas of the three-dimensional feature extractor，ensuring that the three-dimensional feature extractor can extract features that satisfy various target detection. In addition，considering that it is challenging to adapt the existing two-dimensional feature fusion backbone to the center-based head，in this paper self-calibrated convolution and large kernel attention modules are introduced in to effectively extract point cloud features of the target area，which are then gathered into the center point area ，thereby improving the algorithm's recall and accuracy of the target. The proposed algorithm in this article is trained and experimentally verified on the large-scale public dataset of nuScenes. Compared with the benchmark algorithm，mAP and NDS are increased by 5.97% and 3.62% respectively. At the same time，the actual road experiments with the proposed algorithm are conducted on a self-built vehicle platform，further proving the effectiveness of the proposed algorithm. © 2024 SAE-China. All rights reserved.
引用
收藏
页码：1208 / 1218and1238
相关论文
共 35 条
  • [1] WANG Hai, Yang LI, CAI Yingfeng, Et al., 3D real⁃time vehicle tracking based on lidar［J］, Automotive Engineering, 43, 7, pp. 1013-1021, (2021)
  • [2] LOU Xinyu, WANG Hai, CAI Yingfeng, Et al., A research on an algorithm for real-time detection and classification of road obstacle by using 64-line lidar［J］, Automotive Engineering, 41, 7, pp. 779-784, (2019)
  • [3] PointNet：deep learning on point sets for 3D classification and segmentation［C］, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 652-660, (2017)
  • [4] PointNet++：deep hierarchical feature learning on point sets in a metric space［J］, Advances in Neural Information Processing Systems, 30, (2017)
  • [5] LI H., PointrCNN：3D object proposal generation and detection from point cloud［C］, Proceedings of the IEEE/ CVF Conference on Computer Vision and Pattern Recognition, pp. 770-779, (2019)
  • [6] ZHANG Y, Et al., Not all points are equal：learning highly efficient point-based detectors for 3D lidar point clouds ［C］, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 18953-18962, (2022)
  • [7] GONG Zhangpeng, WANG Guoye, YU Shi, The algorithm of multi⁃category object recognition in road scene based on voxel network［J］, Automotive Engineering, 43, 4, pp. 469-477, (2021)
  • [8] JIANG L, Et al., PV-RCNN：point-voxel feature set abstraction for 3D object detection［C］, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10529-10538, (2020)
  • [9] SHI S，, JIANG L, Et al., PV-RCNN++：point-voxel feature set abstraction with local vector representation for 3D object detection［J］, International Journal of Computer Vision, 131, 2, pp. 531-551, (2023)
  • [10] Faster R-CNN：towards real-time object detection with region proposal networks［J］, IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 6, pp. 1137-1149, (2016)
1234