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

• 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
• ISSN: 1573-773X; 1370-4621; 1573-773X
• DOI: 10.1007/s11063-024-11506-2

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).