VB-Net: Voxel-Based Broad Learning Network for 3D Object Classification

Point clouds have been widely used in three-dimensional (3D) object classification tasks, i.e., people recognition in unmanned ground vehicles. However, the irregular data format of point clouds and the large number of parameters in deep learning networks affect the performance of object classificat...

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Bibliographic Details
Published in:Applied sciences 2020-10, Vol.10 (19), p.6735
Main Authors: Liu, Zishu, Song, Wei, Tian, Yifei, Ji, Sumi, Sung, Yunsick, Wen, Long, Zhang, Tao, Song, Liangliang, Gozho, Amanda
Format: Article
Language:English
Subjects:
3D object classification
broad learning system
Classification
Datasets
Deep learning
Design and construction
Fourier transforms
Machine learning
Methods
Model accuracy
Neural networks
Object recognition (Computers)
Pattern recognition
point cloud
Principal components analysis
Researchers
Unmanned ground vehicles
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Summary:Point clouds have been widely used in three-dimensional (3D) object classification tasks, i.e., people recognition in unmanned ground vehicles. However, the irregular data format of point clouds and the large number of parameters in deep learning networks affect the performance of object classification. This paper develops a 3D object classification system using a broad learning system (BLS) with a feature extractor called VB-Net. First, raw point clouds are voxelized into voxels. Through this step, irregular point clouds are converted into regular voxels which are easily processed by the feature extractor. Then, a pre-trained VoxNet is employed as a feature extractor to extract features from voxels. Finally, those features are used for object classification by the applied BLS. The proposed system is tested on the ModelNet40 dataset and ModelNet10 dataset. The average recognition accuracy was 83.99% and 90.08%, respectively. Compared to deep learning networks, the time consumption of the proposed system is significantly decreased.
ISSN:2076-3417
2076-3417
DOI:10.3390/app10196735