融合单目深度和RTK定位的电力线弧垂测量方法
Integration of monocular depth and RTK localization for electric power line sag measurement method
郭嘉琪 1景超 2李雪薇 3王慧民 3张兴忠 1程永强4
作者信息
- 1. 山西省能源互联网研究院 太原 030000;太原理工大学软件学院 晋中 030600
- 2. 山西省能源互联网研究院 太原 030000;西安交通大学人工智能学院 西安 710049
- 3. 山西省能源互联网研究院 太原 030000
- 4. 山西省能源互联网研究院 太原 030000;太原理工大学电子信息与光学工程学院 晋中 030600
- 折叠
摘要
现有电力线路弧垂测量方法操作繁琐且智能化程度低,提出一种融合单目深度和RTK定位的电力线弧垂测量方法.首先,无人机拍摄电力线路关键点图像,将其输入构建的单目深度估计模型EleDep-Net生成对应深度图,该模型嵌入带状金字塔模块和边界融合注意力模块,使模型能精准地捕捉导线上下文语义信息;其次,引入深度矫正算法进一步修正深度图中的深度值,根据修正后的深度值得到关键点深度信息;最后,结合无人机RTK定位和关键点深度信息,在参考坐标系中生成关键点空间坐标进而拟合出导线抛物线公式,通过该公式计算出导线弧垂.在配网线路真实环境中进行测试,结果表明本方法在保证测量相对误差小于5%的前提下,作业效率明显提高,有较高的工程应用价值.
Abstract
The existing methods for measuring the sag of power lines are cumbersome and lack a high level of automation.This paper proposes a power line sag measurement method that integrates monocular depth and RTK positioning.Firstly,unmanned aerial vehicles capture key images of power line routes,and these images are input into the constructed monocular depth estimation model,EleDep-Net,to generate corresponding depth maps.The model incorporates a strip-like pyramid module and a boundary fusion attention module to accurately capture semantic information in the context of the conductors.Secondly,a depth correction algorithm is introduced to further refine the depth values in the depth map,obtaining depth information for the key points.Finally,by combining UAV RTK positioning and key point depth information,spatial coordinates for the key points are generated in the reference coordinate system,and a parabolic formula is fitted to derive the sag of the power line.Tested in a real-world environment of distribution network lines,the results indicate that this method significantly improves operational efficiency while ensuring a relative measurement error of less than 5%,demonstrating its high engineering application value.
关键词
单目深度估计/RTK定位/深度图/抛物线模型/弧垂测量Key words
monocular depth estimation/RTK positioning/depth map/parabolic model/sag measurement引用本文复制引用
基金项目
山西省重点研发计划项目(2022ZDYF100)
出版年
2024
NETL
NSTL
万方数据