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.

中文翻译：

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使用低成本无人机和航空成像进行电网自主异常检测的真实测试平台


电网等关键公用基础设施规模庞大（数百公里）、线性且全年 24×7 运行。因此，使用低成本无人机和航空成像进行维护检查越来越受欢迎。为了拥有低成本的框架，所使用的相机质量不是高质量的或立体相机的。此外，所使用的传感器的种类和效率也受到限制。电网的 3D 重建将有助于改善接入、检测异常（损坏）并减少投影误差。在杂乱的背景中估计电线等线状物体的深度具有挑战性。杂乱的背景包括树木、人行道、绿地和人造物体。在本文中，我们提出了一种使用基于无人机的航空图像进行电网 3D 异常检测的有效框架。该框架使用上下文信息来适应航空成像中不可避免的不同非线性运动。所提出的工作是在使用由非立体航空相机和微型无人机组成的低成本框架捕获的实际数据上进行测试的。