Academic Journal of Computing & Information Science, 2023, 6(5); doi: 10.25236/AJCIS.2023.060510.
Pan Xiang, Li Yongqiang, Li Jiajia, Yang Junli, Zang Jing
School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, Henan, 454000, China
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.
PointNet; Local feature; Graph convolution; Feature combination; Rod-shaped ground objects; Vehicle-borne LiDAR
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.
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