Automatic Assessment and Prediction of the Resilience of Utility Poles Using Unmanned Aerial Vehicles and Computer Vision Techniques

摘要: The utility poles of electric power distribution lines are very vulnerable to many natural hazards, while outages due pole failures can lead adverse economic and social consequences. Utility companies, therefore, need monitor the conditions regularly predict their future accurately promptly operate system continuously safely. This article presents a novel monitoring method that uses state-of-the-art deep learning computer vision methods meet need. proposed automatically captures current inclination angles using an unmanned aerial vehicle. calculates bending moment exerted on wind gravitational forces, as well cable weight, compare it with rupture. also includes machine learning-based model is built by support vector resilience after event in faster manner. three modules effective tools classify expected enable companies to increase systems.