Preview

«System analysis and applied information science»

Advanced search

Lake detection algorithm in point clouds of the lidar image based on three-dimensional convolutional neural network

https://doi.org/10.21122/2309-4923-2022-1-9-11

Abstract

An algorithm for detecting lakes in a point cloud of a lidar image based on a three-dimensional convolutional neural network is proposed. The contours of the lakes were extracted from the point clouds of the lidar image and their geometric characteristics were determined using the chain code algorithm. The accuracy of the proposed algorithm for identifying lakes from clouds of laser scanning points was 96.34%. The proposed algorithm can calculate and analyze information about the shape of lakes.

About the Author

Xu Shanshan
Belarusian National Technical University
Belarus


References

1. Zhou Y, Tuzel O. Voxelnet: end-to-end learning for point cloud based 3D object detection/ Zhou Y, Tuzel O. Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2018 p. 4490-4499.

2. Uy M. A., Lee G. H. PointNetVLAD: deep point cloud based re-trieval for large-scale place recognition / Uy M.A., Lee G.H. Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2018 – p. 4470-4479.

3. Qi C.R., Su H., Mo K., et al. PointNet: deep learning on point sets for 3D classification and segmentation/ Qi C.R., Su H., Mo K. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2017 – p. 77-85.

4. Qi C. R.,Yi L., Su H., et al. PointNet++: deep hierarchical fea-ture learning on point sets in a metric space / Qi C. R., Yi L., Su H Proceedings of the 2017 International Conference on Neural Information Processing Systems. NewYork: Curran Associates Inc., 2017 – p. 5099-5108

5. Wang W., Yu R., Huang Q., et al. SGPN: similarity group pro-posal network for 3D point cloud instance segmentation/ Wang W., Yu R., Huang Q. Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2018 – p.2569-2578.

6. Shen Y., Feng C., Yang Y., et al. Mining point cloud local structures by kernel correlation and graphpooling / Shen Y., Feng C., Yang Y. Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2018 p. 45484557.

7. Landrieu L., Simonovsky M. Large-scale point cloud semantic segmentation with superpoint graphs / Landrieu L., Simonovsky M. Proceedings of the 2018IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2018 – p. 45584567.

8. Huang Q., Wang W., Neumann U. Recurrent slice networks for 3D segmentation of point clouds/ Huang Q., Wang W., Neumann U. Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2018 – p. 26262635.

9. Huang J., You S. Point cloud labeling using 3D convolutional neural network / Huang J., You S. Proceedings of the 3rd International Conference on Pattern Recognition. Piscataway: IEEE, 2016 p.2670-2675.

10. Schnabel R., Wahl R., Klein R. Effi RANSAC for point-cloud shape detection. / Schnabel R., Wahl R., Klein R Computer Graphics Forum, 2007, No. 26(2) – p. 214-226.

11. Whiteson S., Stone P. Evolutionary function approximation for reinforcement learning. / Whiteson S., Stone P. Journal of Machine Learning Research, 2006, No.7 – p. 877-917

12. Nair V., Hinton G.E. Rectifi linear units improve restricted Boltzmann machines / Nair V., Hinton G.E. Proceedings of the 27th International Conference on Machine Learning. Madison: Omnipress, 2010 – p.807-814.


Review

For citations:


Shanshan X. Lake detection algorithm in point clouds of the lidar image based on three-dimensional convolutional neural network. «System analysis and applied information science». 2022;(1):9-11. https://doi.org/10.21122/2309-4923-2022-1-9-11

Views: 327


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 2309-4923 (Print)
ISSN 2414-0481 (Online)