Understanding of leaning utility poles for visual monitoring of power distribution infrastructure

Abstract

Protecting power infrastructure through visual surveillance can assure the safe operation of a power system, especially in unstructured environments where leaning utility poles are particularly inclined to cause large-area blackouts or even personal injury. Current methods place too much emphasis on detection and not enough on understanding leaning postures. However, due to the diversity and uncertainty of leaning utility poles, understanding them remains an urgent problem. Traditional posture estimation via three-dimensional (3D) point clouds is energy-intensive and costly, which limits its adoption in resource-constrained visual surveillance systems. In this study, we present a methodology to understand utility poles, and to estimate their leaning postures using a low-cost monocular camera. Edges and lines are extracted. Through their corresponding proximity and orientation, potential lines of utility poles are estimated. By analyzing relative geometric constraints between potential lines, utility poles are segmented and corresponding leaning angles are estimated, which is helpful to make risk-informed decisions to make leaning utility poles resilient. The approach requires neither prior training, nor calibration or adjustment of the camera’s internal parameters. It is robust against color and illumination associated with severe weather conditions. The percentage of correctly segmented pixels was compared to the ground truth, demonstrating that the method can successfully understand utility poles, meeting safety monitoring requirements for power infrastructure.