Classification of Airborne LIDAR Intensity Data Using Statistical Analysis and Hough Transform with Application to Power Line Corridors Light Detection and Ranging (LIDAR) has  :6,to assist vegetation management in power  :13,by providing more accurate  :18,of the power line assets and  the :25,corridors. However, the development  :31,for the automatic processing :18,of LIDAR  :38,data, in particular :31,for feature extraction  :18,of :45,raw point cloud :38,data, is in still in  :55,In this paper, we take advantage :18,of  and :63,try :6,to classify ground and  :13,by :70,statistically analyzing the skewness  :18,of the :76,intensity data. Moreover, the  is :83,employed :6,to detected power lines from  :91,object points. The experimental results show  :18,of :98,our methods and indicate that  :105,were obtained :13,by using LIDAR :76,intensity  :112,elevation data.

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.