复杂场景下的电力线自动提取  

Automatic power line extraction algorithm in complex scene

作　　者：赵延峰 胡耀垓[1] 王先培[1] 赵乐[1] ZHAO Yanfeng;HU Yaogai;WANG Xianpei;ZHAO Le(School of Electronic Information,Wuhan University,Wuhan 430070,China)

机构地区：[1]武汉大学电子信息学院,湖北武汉430070

出　　处：《测绘通报》2021年第8期1-6,共6页Bulletin of Surveying and Mapping

基　　金：国家自然科学基金(51707135)。

摘　　要：实现复杂场景下航拍图像的端到端电力线提取是电力线无人机检测的关键。本文通过分析基于深度学习的电力线实例分割实现存在的问题,提出了一种基于改进Mask R-CNN的电力线自动提取算法。首先,根据电力线的线性特征,提出一种线性IoU计算方法,改进Mask R-CNN原有的IoU计算,提高电力线提取性能;然后,将改进后的网络在电力线数据集上进行训练,得到电力线粗提取结果;最后,通过线段编组拟合算法,对粗提取结果进行聚类拟合,以解决电力线断裂和误检的问题。试验结果表明,所提方法能从环境复杂的无人机航拍图像中较为准确地提取完整的电力线。End-to-end power line extraction from aerial images in complex scenes is the key to power line detection by UAVs.In this paper,an automatic power line extraction algorithm based on improved Mask R-CNN is proposed by analyzing the problems existing in the realization of power line instance segmentation based on deep learning.First of all,according to the linear characteristic of power line,this paper puts forward a kind of improved Mask R linear IoU calculation method,a CNN original IoU calculation,improve the performance of power line extraction.Then the improved network on the power line data set for training,power line crude extraction results.Finally through a line marshalling fitting method,the result of the crude extract clustering fitting,to solve the problem of power line breakage and misdetection.Experimental results show that the proposed method can accurately extract the complete power lines from UAV aerial images with complex environment.

关 键 词：电力线提取 无人机 Mask R-CNN 线性IoU 线段编组拟合 

分 类 号：P23[天文地球—摄影测量与遥感]