An Automatic Technique for Power Line Pylon Detection from Point Cloud Data

Abstract

This paper proposes a new pylon detection technique from point cloud data. Two masks are created from the non-ground points that mainly represent trees and power line components. The first mask is the power line mask Mₘ, which contains the power line components and trees and where successive pylons are found connected with wires. The second mask is the pylon mask Mₚ, where successive pylons are found disconnected, and thus is exploited to obtain candidate pylons using a connected component analysis. By contrasting the area, shape and symmetry properties between trees and pylons majority of the false candidates (trees) are removed from Mₚ. Finally, long straight lines that represent wires between successive pylons are extracted from Mₘ and used to remove the remaining trees from Mₚ. Experimental results show that the proposed technique provides a high pylon detection rate in terms of both completeness (100%) and correctness (100%).