Affiliation:
1. Research Center of Intelligent Communication Engineering, School of Electronics and Communication Engineering, Guangzhou University, Guangzhou 510006, China
2. School of Computer Science and Engineering, Macau University of Science and Technology, Taipa, 999078 Macau SAR, China
Abstract
Object detection in point clouds is a critical component in most autonomous driving systems. In this paper, in order to improve the effectiveness of image feature extraction and the accuracy of detection of point clouds, a pillar-based 3D point cloud object detection algorithm with multiattention mechanism is proposed, which includes three attention mechanisms SOCA, SOPA, and SAPI. The results show that the recognition accuracy of the optimized algorithm for cars, pedestrians, and cyclists on KITTI dataset is significantly improved on the detection benchmarks of BEV and 3D. Despite using only LiDAR, our algorithm outperforms PointPillars, which is one of the state-of-the-art algorithms for 3D object detection, with respect to both 3D and BEV view KITTI benchmarks while maintaining a relatively competitive speed.
Funder
International Collaborative Research Program of Guangdong Science and Technology Department
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Information Systems
Cited by
4 articles.
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