山东国土资源2026，Vol.42Issue(5)：45-50,6.DOI:10.12128/j.issn.1672-6979.2026.05.006
基于改进动态图卷积神经网络的人工鱼礁识别提取方法研究
Study on Artificial Reef Identification and Extraction Method Based on Improved Dynamic Graph Convolutional Neural Network
张金营 1管楚 1狄桂栓 1李国华 1张强 1孙栋1
作者信息
- 1. 山东省国土测绘院,山东 济南 250013
- 折叠
摘要
Abstract
Artificial reefs are man-made facilities designed to enhance fish populations,slow down the degradation of natural habitats,and affect the development of Marine biological resources.The corre-sponding monitoring and assessment of the artificial reefs that have been deployed play an important role in protecting fishery resources.The image data based on side-scan sonar exposes the loss of three-dimen-sional information and image deformation.Using multi-beam point clouds for the recognition and extrac-tion of artificial reefs has become a new research direction.In this paper,an improved DGCNN model method has been put forward.A normal vector feature extraction module and a multi-head attention mechanism module have been introduced,and the edge convolution features and normal vector features in DGCNN for focused attention have been fused.In this paper,two representative types of fish reefs have been selected for training and testing.The improved method has been compared with PointNet,PointNet++and DGCNN in terms of results.The experimental results show that the improved DGCNN model outperforms traditional classic methods in the completeness and accuracy of artificial fish reef extraction,and has the advantages of rapid recognition,higher extraction accuracy and higher credibility.关键词
人工鱼礁识别提取/多波束点云/动态图卷积神经网络/法向量特征/多头注意力机制Key words
Artificial reef identification and extraction/multi-beam point cloud/dynamic graph convolu-tional neural network/normal vector feature/multi-head attention mechanism分类
天文与地球科学引用本文复制引用
张金营,管楚,狄桂栓,李国华,张强,孙栋..基于改进动态图卷积神经网络的人工鱼礁识别提取方法研究[J].山东国土资源,2026,42(5):45-50,6.
