A ModelDriven Method for Pylon Reconstruction from Oblique UAV Images Pylons play an important role in the safe operation of power transmission grids. Directly reconstructing pylons from UAV images is still a great challenge due to problems of weak texture, hollowcarved structure, and selfocclusion. This paper presents an automatic modeldriven method for pylon reconstruction from oblique UAV images. The pylons are reconstructed with the aid of the 3D parametric model library, which is represented by connected key points based on symmetry and coplanarity. First, an efficient pylon detection method is applied to detect the pylons in the proposed region, which are obtained by clustering the line segment intersection points. Second, the pylon model library is designed to assist in pylon reconstruction. In the predefined pylon model library, a pylon is divided into two parts: pylon body and pylon head. Before pylon reconstruction, the pylon type is identified by the inner distance shape context (IDSC) algorithm, which matches the shape contours of pylon extracted from UAV images and the projected pylon model. With the a priori shape and coplanar constraint, the line segments on pylon body are matched and the pylon body is modeled by fitting four principle legs and four side planes. Then a Markov Chain Monte Carlo (MCMC) sampler is used to estimate the parameters of the pylon head by computing the maximum probability between the projected model and the extracted line segments in images. Experimental results on several UAV image datasets show that the proposed method is a feasible way of automatically reconstructing the pylon.

Monocular vision navigation for aerial surveillance of power lines based on Deep Neural Networks and Hough transform Surveillance of overhead power line installations can be conveniently addressed using unmanned aerial vehicles (UAV). UAV are robotic platforms able to perform sophisticated tasks such as autonomous flight based on visual information. In this paper, we propose a novel solution to the problem of following a power line autonomously based on monocular vision. The method uses Deep Neural Networks (DNN) and the Hough transform to successfully discern power line images from environmental information, which is an essential result to accomplish fully autonomous visionbased navigation. A simulated navigation test demonstrates the efficiency of the proposed method, in the special condition of following rightangled changes of direction, which is a known restriction in many navigation methods reported in literature. The design of the proposed method is modular and can be incorporated in navigation strategies for automatic surveillance applications.