Title :
Real-time power line extraction from Unmanned Aerial System video images
Author :
Yuee Liu ; Mejias, Luis
Author_Institution :
Cooperative Res. Centre for Spatial Inf. & Australian Res. Centre for Aerosp. Autom., Queensland Univ. of Technol., Brisbane, QLD, Australia
Abstract :
In this paper a real-time vision based power line extraction solution is investigated for active UAV guidance. The line extraction algorithm starts from ridge points detected by steerable filters. A collinear line segments fitting algorithm is followed up by considering global and local information together with multiple collinear measurements. GPU boosted algorithm implementation is also investigated in the experiment. The experimental result shows that the proposed algorithm outperforms two baseline line detection algorithms and is able to fitting long collinear line segments. The low computational cost of the algorithm make suitable for real-time applications.
Keywords :
autonomous aerial vehicles; feature extraction; filtering theory; graphics processing units; inspection; object detection; power cables; power engineering computing; remote sensing; video signal processing; GPU boosted algorithm implementation; active UAV guidance; baseline line detection algorithms; collinear line segments fitting algorithm; computational cost; global information; line extraction algorithm; local information; multiple collinear measurements; real-time vision based power line extraction solution; remote inspection; ridge points; steerable filters; unmanned aerial system video images; Automation; Educational institutions; Fitting; Graphics processing units; Image edge detection; Image segmentation; Real-time systems; Gaussian Kernel; Line Detection; Line Segment Grouping; Power Line; Real-time Application; Ridge Points; Steerable Filter; Unmanned Aerial System;
Conference_Titel :
Applied Robotics for the Power Industry (CARPI), 2012 2nd International Conference on
Conference_Location :
Zurich
Print_ISBN :
978-1-4673-4585-9
DOI :
10.1109/CARPI.2012.6473348