An efficient and accurate bev-based camera/lidar 3d object detection algorithm

Lanhua Hou; Xiaosu Xu; Yiqing Yao

doi:10.1117/12.3014990

8 December 2023 An efficient and accurate bev-based camera/lidar 3d object detection algorithm

Lanhua Hou, Xiaosu Xu, Yiqing Yao

Proceedings Volume 12943, International Workshop on Signal Processing and Machine Learning (WSPML 2023); 1294312 (2023) https://doi.org/10.1117/12.3014990
Event: International Workshop on Signal Processing and Machine Learning (WSPML 2023), 2023, Hangzhou, ZJ, China

Abstract

To overcome the limitations of existing algorithms in both efficiency and accuracy, this paper presents an innovative approach for 3D object detection by leveraging a Bird's Eye View (BEV)-based algorithm. Firstly, we introduce a novel sorted matrix decomposition algorithm inspired by the fixed frustum projection and a Hight-compression-based prime extraction method to improve the efficiency of BEV pooing. This approach effectively mitigates the problem of redundant BEV pooling and results in faster process. Secondly, we propose a channel and spatial adaptive fusion algorithm to enhance location accuracy for distant objects. By intelligently fusing BEV-level LiDAR features and camera features, our method achieves precise detection results for objects located at great distances. Finally, we validate the effectiveness of our proposed algorithm in nuScenes dataset, demonstrating the efficiency and accuracy improvements attained by our proposed method. Our approach contributes to advancing the field of 3D object detection through BEV-based camera/LiDAR fusion and offering substantial gains in both efficiency and accuracy.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Lanhua Hou, Xiaosu Xu, and Yiqing Yao "An efficient and accurate bev-based camera/lidar 3d object detection algorithm", Proc. SPIE 12943, International Workshop on Signal Processing and Machine Learning (WSPML 2023), 1294312 (8 December 2023); https://doi.org/10.1117/12.3014990

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KEYWORDS

Image fusion

Matrices

Object detection

LIDAR

Cameras

Feature fusion

Detection and tracking algorithms

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