Multi-sensor fusion 3D object detection based on channel attention

Jianguo Liu; Gongbo Li; Zhiling Jia; Fuwu Yan; Jie Hu; Youhua Wu; Yunfei Sun

doi:10.1117/12.3014774

4 March 2024 Multi-sensor fusion 3D object detection based on channel attention

Jianguo Liu, Gongbo Li, Zhiling Jia, Fuwu Yan, Jie Hu, Youhua Wu, Yunfei Sun

Proceedings Volume 12981, Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023); 129810R (2024) https://doi.org/10.1117/12.3014774
Event: 9th International Symposium on Sensors, Mechatronics, and Automation (ISSMAS 2023), 2023, Nanjing, China

Abstract

The existing 3D target detection network based on feature layer fusion of multi-view images and lidar point cloud fusion is mostly fused by directly splicing the multi-sensor features output by the backbone or the BEV features under the unified perspective of the two modalities. The features obtained by this method will be affected by the original data feature modality conversion and multi-sensor feature fusion`s effect. Aiming at this problem, a 3D object detection network based on feature fusion based on channel attention is proposed to improve the feature aggregation ability of BEV feature fusion, thereby improving the representation ability of the fused features. The experimental results on the nuScenes open source dataset show that compared with the baseline network, the overall feature grasp of the object is increased, and the average orientation error and average speed error are reduced by 4.9% and 4.0%, respectively. In the process of automatic driving, It can improve the vehicle's ability to perceive moving obstacles on the road, which has certain practical value.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jianguo Liu, Gongbo Li, Zhiling Jia, Fuwu Yan, Jie Hu, Youhua Wu, and Yunfei Sun "Multi-sensor fusion 3D object detection based on channel attention", Proc. SPIE 12981, Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023), 129810R (4 March 2024); https://doi.org/10.1117/12.3014774

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Object detection

Feature fusion

Point clouds

Cameras

Image fusion

LIDAR

Head

Show All Keywords

Keywords/Phrases

Search In:

Publication Years