2009
DOI: 10.1007/s00138-009-0206-y
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Towards automatic power line detection for a UAV surveillance system using pulse coupled neural filter and an improved Hough transform

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Cited by 222 publications
(108 citation statements)
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“…The Hough transform is capable of good anti-noise performance [38] and is less sensitive to partial blockage. As such, it is widely used in the extraction of linear objects such as power lines [39], green houses [40], crop direction [41], and architecture [42]. Windthrown trees have obvious linear characteristics, so the Hough transform was adopted in this study for extraction purposes.…”
Section: Fine Extraction Of Individual Windthrown Treesmentioning
confidence: 99%
“…The Hough transform is capable of good anti-noise performance [38] and is less sensitive to partial blockage. As such, it is widely used in the extraction of linear objects such as power lines [39], green houses [40], crop direction [41], and architecture [42]. Windthrown trees have obvious linear characteristics, so the Hough transform was adopted in this study for extraction purposes.…”
Section: Fine Extraction Of Individual Windthrown Treesmentioning
confidence: 99%
“…The most important thing in this method is that the overhead power line must be in the field of view, otherwise it will fail, e.g. [13][14]. In this paper, an inspection strategy is proposed to track the overhead power lines for the camera on UAVs by combining these two strategies.…”
Section: Introductionmentioning
confidence: 99%
“…In our work, a particle filter [28][29] is employed to track the parameters (θ, a, h , k) in Hough space, thus reducing the search time. Each pixel coordinate of edge point is plugged into parabola expression (13) to vote finding the peaks of Acc i (θ i , a i , h i , k i ). Once the parabola lines have been detected, the overhead power lines can be located.…”
mentioning
confidence: 99%
“…The success of these approaches depends on the accuracy of the edges while line thickness is not considered. Although this family of methods has been largely applied in many real contexts [27][28][29][30], its main drawback is the strict requirement of the complete specification of the target object's exact shape to achieve precise localization, which is often difficult and not available for complex curvilinear structures in practice.…”
Section: Introductionmentioning
confidence: 99%