Sparse Embedded Convolution Based Dual Feature Aggregation 3D Object Detection Network

The algorithm design of compatible detection speed and accuracy based on LiDAR point clouds is a challenging issue in various practical applications of 3D object detection, including the field of autonomous driving. This paper designs a single-stage object detection algorithm that is lightweight and...

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Bibliographic Details
Published in:Neural processing letters 2024-02, Vol.56 (1), p.29, Article 29
Main Authors: Li, Hai-Sheng, Lu, Yan-Ling
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial Intelligence
Cameras
Complex Systems
Computational Intelligence
Computer networks
Computer Science
Error detection
Feature extraction
Localization
Methods
Object recognition
Semantics
Sensors
Three dimensional models
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Summary:The algorithm design of compatible detection speed and accuracy based on LiDAR point clouds is a challenging issue in various practical applications of 3D object detection, including the field of autonomous driving. This paper designs a single-stage object detection algorithm that is lightweight and compatible with detection speed and accuracy for the above issue. To achieve these objectives, we propose a framework for a 3D object detection algorithm using a single-stage detection network as the backbone network. Firstly, we design a dual feature extraction module to reduce the occurrence of vehicle miss and error detection problems. Then, we use a multi-scale feature fusion scheme to fuse feature information with different scales. Furthermore, we design a data enhancement scheme suitable for this network architecture. Experimental results in the KITTI dataset show that the proposed method achieves improvement ratios of 38.5% for the detection speed and 2.88% ∼ 13.65% in terms of the average precision of vehicle detection compared to the existing algorithm based on single-stage object detection (SECOND).
ISSN:1573-773X
1370-4621
1573-773X
DOI:10.1007/s11063-024-11506-2