FAST POWER LINE DETECTION AND LOCALIZATION USING STEERABLE FILTER
FOR ACTIVE UAV GUIDANCE
Yuee Liu * *, Luis Mejias*, Zhengrong Li"
* Cooperative Research Centre for Spatial Information and Australian Research Centre for Aerospace Automation at
Queensland University of Technology, 22-24 Boronia Road, Eagle Farm, Brisbane, QLD, 4009 Australia -
(yuee.liu,luis.mejias)@qut.edu.au
b ROAMES, Ergon Energy, 61 Mary Street, Brisbane, QLD 4000, Australia - eric.lizr@roames.com.au
Commission III, Working Group VII
KEY WORDS: line detection, steerable filter, oriented filter, Gaussian kernel, power line, UAV guidance
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.
1. INTRODUCTION
In recent years, 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, ef al.,
2008). UAVs for this type of tasks offer benefits such as
reduction in the cost per kilometre of infrastructure inspected.
However, currently they have payload limitations in size,
weight and power (SWaP). For this reason, in some
circumstances onboard sensors used typically for inspection or
data collection can be seamlessly used for navigation purposes.
This duality of purpose provides benefits when it comes to
performing a more accurate inspection task. 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. However in practice, network GIS data
available is often incomplete, inaccurate or even non-existent in
some regions. Vision-based method provides a good alternative
for navigating UAVs directly over power line networks,
especially in GPS-denied environments. In vision-based active
UAV guidance, images taken from a onboard video camera are
used to estimate the relative position of the plane with regards
to the objects and assist the UAV navigation. The challenge for
vision-based navigation is the real-time detection and
localization aspects.
Objects of interest used for UAV guidance vary from case to
case. In this paper, we consider object of interest power lines,
and therefore their location in the sensor frame is proposed for
UAV guidance. Using power lines to achieve active UAV
guidance will be very beneficial to perform more advanced and
optimal aerial inspection tasks. From the sensor perspective, a
power line is a linear feature with specific width and length that
changes with the sensor height.
* Corresponding author.
Linear feature detection is an important field in computer vision
and has been intensively investigated in the past few years
(Akinlar & Topal, 2011;Nieto, ef al, 2011;Von Gioi, et al.,
2010). 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). More recently, a fast version called
kernel-based Hough transform has been proposed by using an
efficient voting scheme, but produces infinitely long lines rather
than line segments (Fernandes & Oliveira, 2008). Moreover, it
detects more false positive lines and the line position is shifted
from the original position. At gradient/edge level, Line Segment
Detector (LSD) is a technique that uses connected component
analysis (CCA) on quantized gradient orientation to obtain co-
linear pixels, then calculate eigenvector of these pixels as line
segment (Von Gioi, et al., 2010). Although 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). It is the
fastest line segment approaches to the best of our knowledge.
However, both of them respond to all kinds of edges and
generate two edge lines for a line, which make them unsuitable
for power line detection. Some authors have considered lines as
an object with two parallel edges. Koller et al. used two first-
order Gaussian derivative for the left and right line sides
respectively, and combined the response of the two filters in a
nonlinear way as the final response of line (Koller, ef al., 1995).
Line can also be estimated by extracting the centre line or ridge.
Steger computed ridge points by approximating the image with
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