A Method Combining Line Detection and Semantic Segmentation for Power Line Extraction from Unmanned Aerial Vehicle Images

Zhengli Zuo,Qing Dong,Wenbo Zhao

doi:10.3390/rs14061367

Abstract

Power line extraction is the basic task of power line inspection with unmanned aerial vehicle (UAV) images. However, due to the complex backgrounds and limited characteristics, power line extraction from images is a difficult problem. In this paper, we construct a power line data set using UAV images and classify the data according to the image clutter (IC). A method combining line detection and semantic segmentation is used. This method is divided into three steps: First, a multi-scale LSD is used to determine power line candidate regions. Then, based on the object-based Markov random field (OMRF), a weighted region adjacency graph (WRAG) is constructed using the distance and angle information of line segments to capture the complex interaction between objects, which is introduced into the Gibbs joint distribution of the label field. Meanwhile, the Gaussian mixture model is utilized to form the likelihood function by taking the spectral and texture features. Finally, a Kalman filter (KF) and the least-squares method are used to realize power line pixel tracking and fitting. Experiments are carried out on test images in the data set. Compared with common power line extraction methods, the proposed algorithm shows better performance on images with different IC. This study can provide help and guidance for power line inspection.

Highlights

LSDthe detection segmentation and fitting after multi-scale segmentation and compare the results obtained by the proposed method with those of several common unmanned aerial vehicle (UAV) image the results bymethods
We discuss the power line extraction accuracy through Kalman filter (KF) tracking and fitting after multi-scale line segment detector (LSD) detection and weighted region adjacency graph (WRAG)-object-based Markov random field (OMRF) segmentation and compare the results obtained by the proposed method with those of several common UAV image power line extraction methods
The selected comparison methods include: (1) the detection method based on the improved Hough transform (IHT) proposed by Li et al [9], which uses knowledge-based line clustering to refine the detection results in the Hough space; (2) the cluster Radon transform (CRT) detection method proposed by Chen et al [18], which uses the cluster index to enhance the anti-noise ability of the Radon transform; (3) the power line extraction method based on optimized LSD (OLSD) proposed by Ju et al [45], which detects the object directly through the straight-line features of the power line; and

Summary

Introduction

Power system patrol inspection is an important method for transmission line maintenance, as well as guaranteeing the safe and stable operations of the power system. And accurately extracting power lines from UAV images with complex backgrounds is the core step of UAV inspection. The main reasons for this are as follows [7–9]: (1) power line extraction algorithms can provide theoretical support for UAV automatic line inspection, automatic data acquisition, and field-of-view control; (2) the power line extraction algorithm can be applied to UAV flight obstacle avoidance systems in order to ensure the flight safety of the UAV in complex power line corridor environments; and (3) power line extraction is one of the necessary steps for potential fault diagnosis related to a variety of conductor bodies, such as fracture detection, sag calculation, icing thickness measurement, dangerous. It is necessary to separate the background from the foreforeground before line detection.This. The calculation formula is the segmentation threshold of the foreground and background.

Methods

Results

Conclusion