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Academic Journal of Computing & Information Science, 2023, 6(5); doi: 10.25236/AJCIS.2023.060510.

Research on Classification of Rod-Shaped Ground Objects in Road Scene Based on Multi-Feature Associated Network

Author(s)

Pan Xiang, Li Yongqiang, Li Jiajia, Yang Junli, Zang Jing

Corresponding Author:
Pan Xiang
Affiliation(s)

School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, Henan, 454000, China

Abstract

Vehicular LiDAR technology provides powerful technical support for the accurate acquisition of rod-shaped ground objects' spatial information in road scenes. However, how to solve the accurate extraction and classification of rod-shaped ground objects is always a technical problem to be solved. Aiming at the above problems, this paper proposes a MFA-Net based on improved PointNet Network. Firstly, PointNet is used to extract point features and global features from the input data. Then, the local information of the point cloud is extracted by the joint module of high and low layer features constructed by the upsampling method, and the point features, global features and local features are fused into joint features. Finally, the accurate classification of point clouds is achieved by the fully connected layer. Experimental analysis shows that the proposed algorithm model has achieved good results on the public datasets ModelNet40 and ModelNet10, and the classification accuracy on the self-made rod-like ground object dataset Rod5 is 99.0%, which verifies the excellent classification performance and robustness of the proposed algorithm model.

Keywords

PointNet; Local feature; Graph convolution; Feature combination; Rod-shaped ground objects; Vehicle-borne LiDAR

Cite This Paper

Pan Xiang, Li Yongqiang, Li Jiajia, Yang Junli, Zang Jing. Research on Classification of Rod-Shaped Ground Objects in Road Scene Based on Multi-Feature Associated Network. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 5: 75-84. https://doi.org/10.25236/AJCIS.2023.060510.

References

[1] Li Y Q, Li P P, Dong Y H, et al. Automatic Extraction and Classification of Pole-Like Objects from Vehicle Lidar Point Cloud [J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(06):724-735.

[2] Lu X S, Liu R F, Tian M Y, et al. The Improved Mathematical Morphology Method is Used to Carry out Vehicular Laser Point Cloud Ground Filtering[J]. Geomatics and Information Science of Wuhan University, 2014, 39(05):514-519.

[3] Su H, Maji S, Kalogerakis E, et al. Multi-view convolutional neural networks for 3d shape recognition[C].Proceedings of the IEEE International Conference on Computer Vision, 2015: 945-953. 

[4] Qi C R, Su H, Niessner M, et al. Volumetric and multi-view cnns for object classification on 3d data [C].Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 5648-5656.

[5] Cao Z, Huang Q, Karthik R. 3D object classification via spherical projections[C]//2017 International Conference on 3D Vision (3DV). IEEE, 2017: 566-574.

[6] Feng Y, Zhang Z, Zhao X, et al. GVCNN: group-view convolutional neural networks for 3d shape recognition[C].Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 264-272. 

[7] Yu T, Meng J, Yuan J. Multi-view harmonized bilinear network for 3d object recognition [C]. //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018:186-194.

[8] Li Z, Wang H, Li J. Auto-MVCNN: neural architecture search for multi-view 3d shape recognition [J]. 2020, Arxiv Preprint Arxiv: 2012. 05493.

[9] Maturana D, Scherer S. Voxnet: a 3d convolutional neural network for real-time object recognition[C]. 2015 IEEE/ RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2015: 922-928. 

[10] Wu Z, Song S, Khosla A, et al, 3D shapeNets: a deep representation for volumetric shapes[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015:1912-1920.

[11] Huang J, You S. Point cloud labeling using 3d convolutional neural network[C]//2016 23rd International Conference on Pattern Recognition (ICPR). IEEE, 2016: 2670-2675.

[12] Riegler G, Osman Ulusoy A, Geiger A. Octnet: learning deep 3d representations at high resolutions[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017: 3577-3586. 

[13] Hua B S, Tran M K, Yeung S K. Pointwise convolutional neural networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 984-993.

[14] Le T, Duan Y. Pointgrid: A deep network for 3d shape understanding[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 9204-9214.

[15] Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks [J]. Arxiv Preprint Arxiv: 1609.02907, 2016.

[16] Wang C, Samari B, Siddiqi K. Local spectral graph convolution for point set feature learning[C]. // Proceedings of the European Conference on Computer Vision (ECCV). 2018: 52-66. 

[17] Te G, Hu W, Zheng A, et al. Rgcnn: regularized graph cnn for point cloud segmentation [C]. Proceedings of the 26th ACM International Conference on Multimedia, 2018: 746-754.

[18] Wang Y, Sun Y, Liu Z, et al. Dynamic graph cnn for learning on point clouds [J]. ACM Transactions on Graphics (TOG), 2019, 38(5): 1-12. 

[19] Liang Z, Yang M, Deng L, et al. Hierarchical depthwise graph convolutional neural network for 3d semantic segmentation of point clouds[C].2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019: 8152-8158.

[20] Lu Q, Chen C, Xie W, et al. PointNGCNN: deep convolutional networks on 3d point clouds with neighborhood graph filters [J]. Computers & Graphics, 2020, 86: 42-51. 

[21] Qi C R, Su H, Mo K, et al. Pointnet: deep learning on point sets for 3d classification and segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017: 652-660.

[22] Qi C R, Yi L, Su H, et al. Pointnet++: deep hierarchical feature learning on point sets in a metric space [J]. Advances in Neural Information Processing Systems, 2017, 30.

[23] Li J, Chen B M, Hee Lee G. So-net: self-organizing network for point cloud analysis [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018: 9397-9406.

[24] Li Y, Bu R, Sun M, et al. PointCNN: convolution on x-transformed points[C]. Advances in Neural Information Processing Systems, 2018:820-830.

[25] Bai J, Xu H. MSP-Net: Multi-Scale Point Cloud Classification Network [J]. Journal of Computer-Aided Design & Computer Graphics, 2019, 31(11):1917-1924. 

[26] Liu Y, Fan B, Xiang S, et al. Relation-Shape convolutional neural network for point cloud analysis [C]. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2019:8895-8904.

[27] Li R, Li X, Heng P, et al. Point Augment: an auto-augmentation framework for point cloud classification[C].Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020:6377-6386.

[28] Yang B S, Han X, Dong Z. A Deep Learning Network for Semantic Labeling of Large-Scale Urban Point Clouds [J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(08):1059-1067. 

[29] Liang Z M, Zhai Z L, Zhou W. 3D Point Clouds Classific-ation Based on Multi -Scale Dynamic Graph Convolution Network. [J]. Computer Applicati-ons and Software, 2021, 38(05):263-267+306.