Abstract
Abstract. In this paper we present a fast power line detection and localisation algorithm as well as propose a high-level guidance architecture for active vision-based Unmanned Aerial Vehicle (UAV) guidance. The detection stage is based on steerable filters for edge ridge detection, followed by a line fitting algorithm to refine candidate power lines in images. The guidance architecture assumes an UAV with an onboard Gimbal camera. We first control the position of the Gimbal such that the power line is in the field of view of the camera. Then its pose is used to generate the appropriate control commands such that the aircraft moves and flies above the lines. We present initial experimental results for the detection stage which shows that the proposed algorithm outperforms two state-of-the-art line detection algorithms for power line detection from aerial imagery.
Highlights
There has been a considerable interest in civilian applications of unmanned aerial vehicles (UAVs), especially in electrical infrastructure inspection and corridor monitoring applications (Li, et al, 2010;Mills, et al, 2010;Rathinam, et al, 2008)
Most existing UAV guidance approaches assume that the location of network assets is known and that GPS can provide highly accurate real-time position information
Odd-order filters are used for edge detection, while even-order filters are for ridge detection, the second-derivative Gaussian is chosen as we focus on ridge detection for power line detection
Summary
There has been a considerable interest in civilian applications of unmanned aerial vehicles (UAVs), especially in electrical infrastructure inspection and corridor monitoring applications (Li, et al, 2010;Mills, et al, 2010;Rathinam, et al, 2008). Most of the recently proposed methods are based on either gradient/edge (Akinlar & Topal, 2011;Fernandes & Oliveira, 2008;Nieto, et al, 2011;Von Gioi, et al, 2010) or ridge/valley information(Jang & Hong, 2002;Koller, et al, 1995;Steger, 1998) Another well known approach is the Hough transform (Hough, 1962). LSD produces accurate line segment, the involved region growing on the whole image makes it computationally expensive and unsuitable for real-time applications Another gradient/edge approach is the Edge Drawing algorithm (EDLines) which extracts lines from the edge pixel chains based on the least squares line fitting method (Akinlar & Topal, 2011).
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
