Automatic Utility Pole Inclination Angle Measurement Using Unmanned Aerial Vehicle and Deep Learning
Automatic Utility Pole Inclination Angle Measurement Using Unmanned Aerial Vehicle and Deep Learning
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发表时间：
2019
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通讯作者：
Zanbo Zhu;Jing Zhang;M. Alam;Berna Eren Tokgoz;Seokyon Hwang Construction
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作者：
Zanbo Zhu;Jing Zhang;M. Alam;Berna Eren Tokgoz;Seokyon Hwang Construction
由于电线杆极易受到自然灾害的影响，因此测量配电线路电线杆的倾斜角对于维护配电系统和减少停电至关重要。然而，传统的人工极点检测方法成本高，工作量大。本文提出了一种新型的电线杆监测系统，可根据无人机捕获的图像自动测量电线杆的倾角。利用最先进的深度学习神经网络从无人机街景图像中检测和分割电线杆，并利用计算机视觉技术计算基于分割电线杆的倾角。利用在不同天气条件下拍摄的64幅图像和84根电线杆对所提出的方法进行了评估。电线杆分割精度为93.74%，平均倾角误差为0.59度，表明了电线杆监测系统的有效性。
Measuring the inclination angles of utility poles of the electric power distribution lines is critical to maintain power distribution systems and minimize power outages, because the poles are very vulnerable to natural disasters. However, traditional human-based pole inspection methods are very costly and require heavy workloads. In this paper, we propose a novel pole monitoring system to measure the inclination angle of utility poles from images captured by unmanned aerial vehicle (UAV) automatically. A state-of-the-art deep learning neural network is used to detect and segment utility poles from UAV street view images, and computer vision techniques are used to calculate the inclination angles based on the segmented poles. The proposed method was evaluated using 64 images with 84 utility poles taken in different weather conditions. The pole segmentation accurcy is 93.74% and the average inclination angle error is 0.59 degrees, which demonstate the efficiency of the proposed utility pole monitoring system.