An Entropy-Weighting Method for Efficient Power-Line Feature Evaluation and Extraction from LiDAR Point Clouds

Junxiang Tan,Shaoda Li,Ronghao Yang,Hua Liu,Jianfei Liu,Haojie Zhao

doi:10.3390/rs13173446

Abstract

Power-line inspection is an important means to maintain the safety of power networks. Light detection and ranging (LiDAR) technology can provide high-precision 3D information about power corridors for automated power-line inspection, so there are more and more utility companies relying on LiDAR systems instead of traditional manual operation. However, it is still a challenge to automatically detect power lines with high precision. To achieve efficient and accurate power-line extraction, this paper proposes an algorithm using entropy-weighting feature evaluation (EWFE), which is different from the existing hierarchical-multiple-rule evaluation of many geometric features. Six significant features are selected (Height above Ground Surface (HGS), Vertical Range Ratio (VRR), Horizontal Angle (HA), Surface Variation (SV), Linearity (LI) and Curvature Change (CC)), and then the features are combined to construct a vector for quantitative evaluation. The feature weights are determined by an entropy-weighting method (EWM) to achieve optimal distribution. The point clouds are filtered out by the HGS feature, which possesses the highest entropy value, and a portion of non-power-line points can be removed without loss of power-line points. The power lines are extracted by evaluation of the other five features. To decrease the interference from pylon points, this paper analyzes performance in different pylon situations and performs an adaptive weight transformation. We evaluate the EWFE method using four datasets with different transmission voltage scales captured by a light unmanned aerial vehicle (UAV) LiDAR system and a mobile LiDAR system. Experimental results show that our method demonstrates efficient performance, while algorithm parameters remain consistent for the four datasets. The precision F value ranges from 98.4% to 99.7%, and the efficiency ranges from 0.9 million points/s to 5.2 million points/s.

Highlights

Power networks are significant components of infrastructure that transport electricity from power supply institutions to consumers
To achieve efficient power transmission lines (PTLs) extraction for different platforms and complex scenes, this paper proposes a method based on entropy-weighting feature evaluation (EWFE), which focuses on efficient PTL extraction by using as few salient geometric features as possible and achieving robust extraction with few parameter adjustments
The results indicate that feature evaluation can improve PTL extraction effectively

Summary

Introduction

Power networks are significant components of infrastructure that transport electricity from power supply institutions to consumers. Management and maintenance of power transmission lines (PTLs) are important for stable power supplying [1]. Power transmission line (PTL) inspection mainly includes regular inspection for power component defects to avoid malfunction, such as power failure caused by components breakage [2], and to detect surrounding potential threats, especially vegetation invasion, which may cause loss of power or even forest fire if contact is made with PTLs. traditional inspection methods are much too reliant on artificial observation or manual analysis of aerial photos and videos, which is inefficient and relies on experience [3]. It is a challenging task to detect a wide range of PTLs

Methods

Results

Discussion

Conclusion