Real Testbed for Autonomous Anomaly Detection in Power Grid Using Low-Cost Unmanned Aerial Vehicles and Aerial Imaging

Abstract

Critical utility infrastructure like power grids are vast (in hundreds of kilometers), linear, and operated 24×7 throughout the year. Maintenance inspections using low-cost unmanned aerial vehicles and aerial imaging are therefore gaining popularity. To have low-cost framework, the quality of the camera used is not of high quality or a stereo-rig one. Also, the sensors used are limited in variety and efficiency. 3-D reconstruction of a power grid will help to improve access, detect anomalies (damages), and reduce projection error. The depth estimation of wiry objects, like powerlines, in a cluttered background is challenging. The background clutter includes trees, pavement, greenery patches, and man-made objects. In this article, we propose an efficient framework for 3-D anomaly detection in power grids using UAV-based aerial images. The framework uses context information to become adaptive with different nonlinear movements that are unavoidable in aerial imaging. The proposed work is tested on real-data captured using a low-cost framework consisting of a non-stereo-rig aerial camera and a mini-UAV.