CenterPoint-SE: A Single-Stage Anchor-Free 3-D Object Detection Algorithm With Spatial Awareness Enhancement

Real-time and accurate 3-D object detection is one of the foundational technologies for environmental perception in autonomous vehicles. However, the existing second-stage anchor-based 3-D object detection algorithms have high accuracy, but they are challenging in terms of computation complexity and...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems Vol. 24; no. 10; pp. 10760 - 10773
Main Authors: Wang, Hai, Tao, Le, Cai, Yingfeng, Chen, Long, Li, Yicheng, Sotelo, Miguel Angel, Li, Zhixiong
Format: Journal Article
Language:English
Published: New York IEEE 01.10.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
3D object detection
Accuracy
Algorithms
anchor-free
Autonomous vehicles
Computer networks
Detection algorithms
Feature extraction
Location awareness
Object detection
Object recognition
Perception
point cloud
Point cloud compression
single-stage
space feature enhancement
Training
ISSN:1524-9050, 1558-0016
Online Access:Get full text
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Summary:Real-time and accurate 3-D object detection is one of the foundational technologies for environmental perception in autonomous vehicles. However, the existing second-stage anchor-based 3-D object detection algorithms have high accuracy, but they are challenging in terms of computation complexity and latency. Due to poor perception of spatial features, the accuracy of the existing single-stage anchor-free detection algorithms with low latency are difficult to be implemented into autonomous vehicles. Therefore, we focus on enhancing the spatial perception ability of the anchor-free detection network based on CenterPoints. In this paper, we propose a single-stage anchor-free 3-D object detector CenterPoint-Space-Enhancement (CenterPoint-SE) algorithm and construct an efficient 3-D backbone network to extract fine-grained spatial geometric features by introducing a spatial attention mechanism and residual structure. At the same time, a powerful spatial semantic feature fusion module, the enhancement of feature fusion (EF-Fusion), is designed. In addition, we add a lightweight IoU prediction branch to improve the algorithm's perception of various object sizes. Finally, we add a foreground point segmentation auxiliary training branch to enable the 3-D backbone to obtain object boundary features. We use the ONCE dataset to train and validate the proposed model, and the results showed that the proposed CenterPoint-SE achieves 70.33 mAP and an inference speed of 17.15 FPS, outperforming other methods.
Bibliography:ObjectType-Article-1
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ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2023.3273817