摘要
为应对当前利用二维数据进行三维建模出现的时间周期较长且处理工作繁杂等问题,研究在分析无人机倾斜摄影测量技术应用特点的基础上,构建了三维虚拟景观系统。而后在TensorFlow平台下设计了改进的卷积神经网络(Convolutional Neural Networks,CNN)结构,并将其应用于三维虚拟景观系统中。结果表明,在自制的三维景观数据集中,改进CNN对于绿化景观的特征提取准确率高达96.36%。在可视化效果和功能性验证中,所提出的三维虚拟景观系统评分可达97.5分,并且最短耗时仅为108 s。说明所设计的三维虚拟景观系统能够对多种景观类型的特征进行有效提取,具有更好的可视化效果和功能性,为实景三维模型和智慧城市的建设提供了方法参考。
In order to address the problems of long time cycles and complex processing work in the current use of 2D data for 3D modeling,a 3D virtual landscape system was constructed based on the analysis of the application characteristics of unmanned aerial vehicle tilt photogrammetry technology.Subsequently,an improved Convolutional Neural Networks(CNN)structure was designed on the TensorFlow platform and applied to a 3D virtual landscape system.The results show that in the self-made 3D landscape dataset,the improved CNN has a high accuracy rate of 96.36%for feature extraction of green landscapes.In the visual effect and functional verification,the proposed 3D virtual landscape system can score up to 97.5 points,and the shortest time is only 108s.It shows that the designed 3D virtual landscape system can effectively extract the features of various landscape types,and has better visualization effect and functionality,which provides a method reference for the construction of real 3D model and smart city.
作者
邢艳艳
蔡佩
娄建新
赵晨
XING Yanyan;CAI Pei;LOU Jianxin;ZHAO Chen(Guangzhou Vocational University of Science and Technology,Guangzhou 510000,China;Guangdong Polytechnic of Water Resources and Electric Engineering,Guangzhou 510000,China)
出处
《自动化与仪器仪表》
2024年第2期191-194,共4页
Automation & Instrumentation
基金
广东省哲学社科规划2022年度青年课题成果之一(GD22YYS04)。
关键词
无人机
倾斜摄影
三维
虚拟景观
卷积神经网络
drones
oblique photography
3D
virtual landscape
convolutional neural network
